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Xiaotong Shen

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20 papers

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JMLR Journal 2024 Journal Article

Causal Discovery with Generalized Linear Models through Peeling Algorithms

Minjie Wang
Xiaotong Shen
Wei Pan

This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes, including discrete, continuous, and mixed data. Causal discovery often faces challenges due to unmeasured confounders that hinder the identification of causal relationships. The proposed approach addresses this issue by developing two peeling algorithms (bottom-up and top-down) to ascertain causal relationships and valid instruments. This approach first reconstructs a super-graph to represent ancestral relationships between variables, using a peeling algorithm based on nodewise GLM regressions that exploit relationships between primary and instrumental variables. Then, it estimates parent-child effects from the ancestral relationships using another peeling algorithm while deconfounding a child's model with information borrowed from its parents' models. The article offers a theoretical analysis of the proposed approach, establishing conditions for model identifiability and providing statistical guarantees for accurately discovering parent-child relationships via the peeling algorithms. Furthermore, the article presents numerical experiments showcasing the effectiveness of our approach in comparison to state-of-the-art structure learning methods without confounders. Lastly, it demonstrates an application to Alzheimer's disease (AD), highlighting the method's utility in constructing gene-to-gene and gene-to-disease regulatory networks involving Single Nucleotide Polymorphisms (SNPs) for healthy and AD subjects. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

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JMLR Journal 2023 Journal Article

Inference for a Large Directed Acyclic Graph with Unspecified Interventions

Chunlin Li
Xiaotong Shen
Wei Pan

Statistical inference of directed relations given some unspecified interventions (i.e., the intervention targets are unknown) is challenging. In this article, we test hypothesized directed relations with unspecified interventions. First, we derive conditions to yield an identifiable model. Unlike classical inference, testing directed relations requires identifying the ancestors and relevant interventions of hypothesis-specific primary variables. To this end, we propose a peeling algorithm based on nodewise regressions to establish a topological order of primary variables. Moreover, we prove that the peeling algorithm yields a consistent estimator in low-order polynomial time. Second, we propose a likelihood ratio test integrated with a data perturbation scheme to account for the uncertainty of identifying ancestors and interventions. Also, we show that the distribution of a data perturbation test statistic converges to the target distribution. Numerical examples demonstrate the utility and effectiveness of the proposed methods, including an application to infer gene regulatory networks. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2023. ( edit, beta )

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JMLR Journal 2020 Journal Article

A Regularization-Based Adaptive Test for High-Dimensional GLMs

Chong Wu
Gongjun Xu
Xiaotong Shen
Wei Pan

In spite of its urgent importance in the era of big data, testing high-dimensional parameters in generalized linear models (GLMs) in the presence of high-dimensional nuisance parameters has been largely under-studied, especially with regard to constructing powerful tests for general (and unknown) alternatives. Most existing tests are powerful only against certain alternatives and may yield incorrect Type 1 error rates under high-dimensional nuisance parameter situations. In this paper, we propose the adaptive interaction sum of powered score (aiSPU) test in the framework of penalized regression with a non-convex penalty, called truncated Lasso penalty (TLP), which can maintain correct Type 1 error rates while yielding high statistical power across a wide range of alternatives. To calculate its p-values analytically, we derive its asymptotic null distribution. Via simulations, its superior finite-sample performance is demonstrated over several representative existing methods. In addition, we apply it and other representative tests to an Alzheimer's Disease Neuroimaging Initiative (ADNI) data set, detecting possible gene-gender interactions for Alzheimer's disease. We also put R package “aispu” implementing the proposed test on GitHub. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2020. ( edit, beta )

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JMLR Journal 2019 Journal Article

Smooth neighborhood recommender systems

Ben Dai
Junhui Wang
Xiaotong Shen
Annie Qu

Recommender systems predict users' preferences over a large number of items by pooling similar information from other users and/or items in the presence of sparse observations. One major challenge is how to utilize user-item specific covariates and networks describing user-item interactions in a high-dimensional situation, for accurate personalized prediction. In this article, we propose a smooth neighborhood recommender in the framework of the latent factor models. A similarity kernel is utilized to borrow neighborhood information from continuous covariates over a user-item specific network, such as a user's social network, where the grouping information defined by discrete covariates is also integrated through the network. Consequently, user-item specific information is built into the recommender to battle the `cold-start” issue in the absence of observations in collaborative and content-based filtering. Moreover, we utilize a “divide-and-conquer” version of the alternating least squares algorithm to achieve scalable computation, and establish asymptotic results for the proposed method, demonstrating that it achieves superior prediction accuracy. Finally, we illustrate that the proposed method improves substantially over its competitors in simulated examples and real benchmark data--Last.fm music data. [abs] [ pdf ][ bib ] &copy JMLR 2019. ( edit, beta )

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ICRA Conference 2018 Conference Paper

Conditional Compatibility Branch and Bound for Feature Cloud Matching

Xiaotong Shen
Marcelo H. Ang
Daniela Rus

In this paper, we consider the problem of data association in feature cloud matching. While Joint Compatibility (JC) test is a widely adopted technique for searching the global optimal data association, it becomes less restrictive as more features are well matched. The early well-matched features contribute little to total matching cost while the gating threshold increases in the chi-square test, which allows the acceptance of bad feature pairings in the last step. In this paper, we propose the Conditional Compatibility (CC) test, which is not only more restrictive than JC test, but also probabilistically sound. The proposed test of a new feature pairing is based on the conditional probability distribution of feature locations given the early pairings. CC test can be added into any JC test based search algorithm, such as Joint Compatibility Branch and Bound (JCBB), Incremental Posterior Joint Compatibility (IPJC) and FastJCBB, without increasing much computational complexity. The more restrictive criterion of accepting a feature pairing, not only helps to reject bad associations, but also bounds the search space, which substantially improves the search efficiency. The real matching experiments justify that our algorithm produces better feature cloud matching results in a more efficient manner.

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ICRA Conference 2018 Conference Paper

Vehicle Detection, Tracking and Behavior Analysis in Urban Driving Environments Using Road Context

Shashwat Verma
You Hong Eng
Hai Xun Kong
Hans Andersen
Malika Meghjani
Wei Kang Leong
Xiaotong Shen
Chen Zhang 0018

We present a real-time vehicle detection and tracking system to accomplish the complex task of driving behavior analysis in urban environments. We propose a robust fusion system that combines a monocular camera and a 2D Lidar. This system takes advantage of three key components: robust vehicle detection using deep learning techniques, high precision range estimation from Lidar, and road context from the prior map knowledge. The camera and Lidar sensor fusion, data association and track management are all performed in the global map coordinate system by taking into account the sensors' characteristics. Lastly, behavior reasoning is performed by examining the tracked vehicle states in the lane coordinate system in which the road context is encoded. We validated our approach by tracking a leading vehicle while it performed usual urban driving behaviors such as lane keeping, stop-and-go at intersections, lane changing, overtaking and turning. The leading vehicle was tracked consistently throughout the 2. 3 km route and its behavior was classified reliably.

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JMLR Journal 2016 Journal Article

A New Algorithm and Theory for Penalized Regression-based Clustering

Chong Wu
Sunghoon Kwon
Xiaotong Shen
Wei Pan

Clustering is unsupervised and exploratory in nature. Yet, it can be performed through penalized regression with grouping pursuit, as demonstrated in Pan et al. (2013). In this paper, we develop a more efficient algorithm for scalable computation and a new theory of clustering consistency for the method. This algorithm, called DC-ADMM, combines difference of convex (DC) programming with the alternating direction method of multipliers (ADMM). This algorithm is shown to be more computationally efficient than the quadratic penalty based algorithm of Pan et al. (2013) because of the former's closed-form updating formulas. Numerically, we compare the DC- ADMM algorithm with the quadratic penalty algorithm to demonstrate its utility and scalability. Theoretically, we establish a finite-sample mis- clustering error bound for penalized regression based clustering with the $L_0$ constrained regularization in a general setting. On this ground, we provide conditions for clustering consistency of the penalized clustering method. As an end product, we put R package prclust implementing PRclust with various loss and grouping penalty functions available on GitHub and CRAN. [abs] [ pdf ][ bib ] &copy JMLR 2016. ( edit, beta )

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IROS Conference 2016 Conference Paper

Fast Joint Compatibility Branch and Bound for feature cloud matching

Xiaotong Shen
Emilio Frazzoli
Daniela Rus
Marcelo H. Ang

In this work, we address the problem of robust data association for feature cloud matching. For matching two feature clouds observed at two different poses, we discover that the covariance matrix of the measurement prediction error can be written as the sum of a low rank matrix and a block diagonal matrix, if we assume that the features are observed independently at each pose. This special structure of the covariance matrix allows us to compute its inverse analytically and efficiently. Together with a good bookkeeping strategy, the complexity of the Joint Compatibility (JC) test is reduced to O(1). Contrary to the approximated JC test, ours is both exact and fast. Based on the efficient JC test algorithm and a branch and bound search procedure, we devise an algorithm, called Fast Joint Compatibility Branch and Bound (FastJCBB), to quickly obtain robust data association. The FastJCBB algorithm is essentially modified from the conventional Joint Compatibility Branch and Bound (JCBB) algorithm and both of these algorithms are able to produce exactly the same data association results. However, with the substantial improvement in the efficiency of JC tests, our FastJCBB algorithm is much faster than the conventional JCBB, especially when matching two large feature clouds. It is reported that our FastJCBB algorithm is more than 740 times faster than the conventional JCBB in carrying out one million JC tests when matching two clouds with about 100 features each. Since both FastJCBB and JCBB share the same branch and bound procedure in exploring the interpretation tree, the search complexity remains exponential. Our main contribution is the significant improvement in the efficiency of exploring each node of the interpretation tree.

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IROS Conference 2015 Conference Paper

Autonomous golf cars for public trial of mobility-on-demand service

Scott Drew Pendleton
Tawit Uthaicharoenpong
Zhuang Jie Chong
James Guo Ming Fu
Baoxing Qin
Wei Liu 0024
Xiaotong Shen
Zhiyong Weng

We detail the design of autonomous golf cars which were used in public trials in Singapore's Chinese and Japanese Gardens, for the purpose of raising public awareness and gaining user acceptance of autonomous vehicles. The golf cars were designed to be robust, reliable, and safe, while operating under prolonged durations. Considerations that went in to the overall system design included the fact that any member of the public had to not only be able to easily use the system, but to also not have the option to use the system in an unintended manner. This paper details the hardware and software components of the golf cars with these considerations, and also how the booking system and mission planner facilitated users to book for a golf car from any of ten stations within the gardens. We show that the vehicles performed robustly throughout the prolonged operations with a small localization variance, and that users were very receptive from the user survey results.

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AIJ Journal 2015 Journal Article

Efficient nonconvex sparse group feature selection via continuous and discrete optimization

Shuo Xiang
Xiaotong Shen
Jieping Ye

Sparse feature selection has proven to be effective in analyzing high-dimensional data. While promising, most existing works apply convex methods, which may be suboptimal in terms of the accuracy of feature selection and parameter estimation. In this paper, we consider both continuous and discrete nonconvex paradigms to sparse group feature selection, which are motivated by applications that require identifying the underlying group structure and performing feature selection simultaneously. The main contribution of this article is twofold: (1) computationally, we develop efficient optimization algorithms for both continuous and discrete formulations, of which the key step is a projection with two coupled constraints; (2) statistically, we show that the proposed continuous model reconstructs the oracle estimator. Therefore, consistent feature selection and parameter estimation are achieved simultaneously. Numerical results on synthetic and real-world data suggest that the proposed nonconvex methods compare favorably against their competitors, thus achieving desired goal of delivering high performance.

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JMLR Journal 2015 Journal Article

Simultaneous Pursuit of Sparseness and Rank Structures for Matrix Decomposition

Qi Yan
Jieping Ye
Xiaotong Shen

In multi-response regression, pursuit of two different types of structures is essential to battle the curse of dimensionality. In this paper, we seek a sparsest decomposition representation of a parameter matrix in terms of a sum of sparse and low rank matrices, among many overcomplete decompositions. On this basis, we propose a constrained method subject to two nonconvex constraints, respectively for sparseness and low-rank properties. Computationally, obtaining an exact global optimizer is rather challenging. To overcome the difficulty, we use an alternating directions method solving a low-rank subproblem and a sparseness subproblem alternatively, where we derive an exact solution to the low-rank subproblem, as well as an exact solution in a special case and an approximated solution generally through a surrogate of the $L_0$-constraint and difference convex programming, for the sparse subproblem. Theoretically, we establish convergence rates of a global minimizer in the Hellinger-distance, providing an insight into why pursuit of two different types of decomposed structures is expected to deliver higher estimation accuracy than its counterparts based on either sparseness alone or low-rank approximation alone. Numerical examples are given to illustrate these aspects, in addition to an application to facial imagine recognition and multiple time series analysis. [abs] [ pdf ][ bib ] &copy JMLR 2015. ( edit, beta )

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YNIMG Journal 2014 Journal Article

Comparison of statistical tests for group differences in brain functional networks

Junghi Kim
Jeffrey R. Wozniak
Bryon A. Mueller
Xiaotong Shen
Wei Pan

Brain functional connectivity has been studied by analyzing time series correlations in regional brain activities based on resting-state fMRI data. Brain functional connectivity can be depicted as a network or graph defined as a set of nodes linked by edges. Nodes represent brain regions and an edge measures the strength of functional correlation between two regions. Most of existing work focuses on estimation of such a network. A key but inadequately addressed question is how to test for possible differences of the networks between two subject groups, say between healthy controls and patients. Here we illustrate and compare the performance of several state-of-the-art statistical tests drawn from the neuroimaging, genetics, ecology and high-dimensional data literatures. Both real and simulated data were used to evaluate the methods. We found that Network Based Statistic (NBS) performed well in many but not all situations, and its performance critically depends on the choice of its threshold parameter, which is unknown and difficult to choose in practice. Importantly, two adaptive statistical tests called adaptive sum of powered score (aSPU) and its weighted version (aSPUw) are easy to use and complementary to NBS, being higher powered than NBS in some situations. The aSPU and aSPUw tests can also be applied to adjust for covariates. Between the aSPU and aSPUw tests, they often, but not always, performed similarly with neither one as a uniform winner. On the other hand, Multivariate Matrix Distance Regression (MDMR) has been applied to detect group differences for brain connectivity; with the usual choice of the Euclidean distance, MDMR is a special case of the aSPU test. Consequently NBS, aSPU and aSPUw tests are recommended to test for group differences in functional connectivity.

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IROS Conference 2014 Conference Paper

Spatio-temporal motion features for laser-based moving objects detection and tracking

Xiaotong Shen
Seong-Woo Kim
Marcelo H. Ang

This paper proposes a spatio-temporal motion feature detection and tracking method using range sensors working on a moving platform. The proposed spatio-temporal motion features are similar to optical flow but are extended on a moving platform with fusion of odometry and show much better classification accuracy with consideration of different uncertainties. In the proposal, the ego motion is compensated by odometry sensors and the laser scan points are accumulated and represented as space-time point clouds, from which the velocities and moving directions can be extracted. Based on these spatio-temporal features, a supervised learning technique is applied to classify the points as static or moving and Kalman filters are implemented to track the moving objects. A real experiment is performed during day and night on an autonomous vehicle platform and shows promising results in a crowded and dynamic environment.

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YNIMG Journal 2014 Journal Article

Testing for association with multiple traits in generalized estimation equations, with application to neuroimaging data

Yiwei Zhang
Zhiyuan Xu
Xiaotong Shen
Wei Pan

There is an increasing need to develop and apply powerful statistical tests to detect multiple traits–single locus associations, as arising from neuroimaging genetics and other studies. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI), in addition to genome-wide single nucleotide polymorphisms (SNPs), thousands of neuroimaging and neuropsychological phenotypes as intermediate phenotypes for Alzheimer's disease, have been collected. Although some classic methods like MANOVA and newly proposed methods may be applied, they have their own limitations. For example, MANOVA cannot be applied to binary and other discrete traits. In addition, the relationships among these methods are not well understood. Importantly, since these tests are not data adaptive, depending on the unknown association patterns among multiple traits and between multiple traits and a locus, these tests may or may not be powerful. In this paper we propose a class of data-adaptive weights and the corresponding weighted tests in the general framework of generalized estimation equations (GEE). A highly adaptive test is proposed to select the most powerful one from this class of the weighted tests so that it can maintain high power across a wide range of situations. Our proposed tests are applicable to various types of traits with or without covariates. Importantly, we also analytically show relationships among some existing and our proposed tests, indicating that many existing tests are special cases of our proposed tests. Extensive simulation studies were conducted to compare and contrast the power properties of various existing and our new methods. Finally, we applied the methods to an ADNI dataset to illustrate the performance of the methods. We conclude with the recommendation for the use of the GEE-based Score test and our proposed adaptive test for their high and complementary performance.

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JMLR Journal 2013 Journal Article

Cluster Analysis: Unsupervised Learning via Supervised Learning with a Non-convex Penalty

Wei Pan
Xiaotong Shen
Binghui Liu

Clustering analysis is widely used in many fields. Traditionally clustering is regarded as unsupervised learning for its lack of a class label or a quantitative response variable, which in contrast is present in supervised learning such as classification and regression. Here we formulate clustering as penalized regression with grouping pursuit. In addition to the novel use of a non-convex group penalty and its associated unique operating characteristics in the proposed clustering method, a main advantage of this formulation is its allowing borrowing some well established results in classification and regression, such as model selection criteria to select the number of clusters, a difficult problem in clustering analysis. In particular, we propose using the generalized cross-validation (GCV) based on generalized degrees of freedom (GDF) to select the number of clusters. We use a few simple numerical examples to compare our proposed method with some existing approaches, demonstrating our method's promising performance. [abs] [ pdf ][ bib ] &copy JMLR 2013. ( edit, beta )

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IROS Conference 2013 Conference Paper

Cooperative perception for autonomous vehicle control on the road: Motivation and experimental results

Seong-Woo Kim
Zhuang Jie Chong
Baoxing Qin
Xiaotong Shen
Zhuoqi Cheng
Wei Liu 0024
Marcelo H. Ang

In this paper, we attempt to develop a reusable framework of cooperative perception for vehicle control on the road that can extend perception range beyond line-of-sight and beyond field-of-view. For this goal, the following problems are addressed: map merging, vehicle identification, sensor multi-modality, impact of communications, and impact on path planning. We provide experimental results using a self-driving vehicle and manned vehicles equipped with the cooperative perception systems that we propose and implement.

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JMLR Journal 2011 Journal Article

Large Margin Hierarchical Classification with Mutually Exclusive Class Membership

Huixin Wang
Xiaotong Shen
Wei Pan

In hierarchical classification, class labels are structured, that is each label value corresponds to one non-root node in a tree, where the inter-class relationship for classification is specified by directed paths of the tree. In such a situation, the focus has been on how to leverage the inter-class relationship to enhance the performance of flat classification, which ignores such dependency. This is critical when the number of classes becomes large relative to the sample size. This paper considers single-path or partial-path hierarchical classification, where only one path is permitted from the root to a leaf node. A large margin method is introduced based on a new concept of generalized margins with respect to hierarchy. For implementation, we consider support vector machines and ψ-learning. Numerical and theoretical analyses suggest that the proposed method achieves the desired objective and compares favorably against strong competitors in the literature, including its flat counterparts. Finally, an application to gene function prediction is discussed. [abs] [ pdf ][ bib ] &copy JMLR 2011. ( edit, beta )

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JMLR Journal 2009 Journal Article

On Efficient Large Margin Semisupervised Learning: Method and Theory

Junhui Wang
Xiaotong Shen
Wei Pan

In classification, semisupervised learning usually involves a large amount of unlabeled data with only a small number of labeled data. This imposes a great challenge in that it is difficult to achieve good classification performance through labeled data alone. To leverage unlabeled data for enhancing classification, this article introduces a large margin semisupervised learning method within the framework of regularization, based on an efficient margin loss for unlabeled data, which seeks efficient extraction of the information from unlabeled data for estimating the Bayes decision boundary for classification. For implementation, an iterative scheme is derived through conditional expectations. Finally, theoretical and numerical analyses are conducted, in addition to an application to gene function prediction. They suggest that the proposed method enables to recover the performance of its supervised counterpart based on complete data in rates of convergence, when possible. [abs] [ pdf ][ bib ] &copy JMLR 2009. ( edit, beta )

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JMLR Journal 2007 Journal Article

Large Margin Semi-supervised Learning

Junhui Wang
Xiaotong Shen

In classification, semi-supervised learning occurs when a large amount of unlabeled data is available with only a small number of labeled data. In such a situation, how to enhance predictability of classification through unlabeled data is the focus. In this article, we introduce a novel large margin semi-supervised learning methodology, using grouping information from unlabeled data, together with the concept of margins, in a form of regularization controlling the interplay between labeled and unlabeled data. Based on this methodology, we develop two specific machines involving support vector machines and ψ-learning, denoted as SSVM and SPSI, through difference convex programming. In addition, we estimate the generalization error using both labeled and unlabeled data, for tuning regularizers. Finally, our theoretical and numerical analyses indicate that the proposed methodology achieves the desired objective of delivering high performance in generalization, particularly against some strong performers. [abs] [ pdf ][ bib ] &copy JMLR 2007. ( edit, beta )

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JMLR Journal 2007 Journal Article

Penalized Model-Based Clustering with Application to Variable Selection

Wei Pan
Xiaotong Shen

Variable selection in clustering analysis is both challenging and important. In the context of model-based clustering analysis with a common diagonal covariance matrix, which is especially suitable for "high dimension, low sample size" settings, we propose a penalized likelihood approach with an L 1 penalty function, automatically realizing variable selection via thresholding and delivering a sparse solution. We derive an EM algorithm to fit our proposed model, and propose a modified BIC as a model selection criterion to choose the number of components and the penalization parameter. A simulation study and an application to gene function prediction with gene expression profiles demonstrate the utility of our method. [abs] [ pdf ][ bib ] &copy JMLR 2007. ( edit, beta )

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