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Jiangwen Sun

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6 papers

2 author rows

NeurIPS Conference 2016 Conference Paper

A Sparse Interactive Model for Matrix Completion with Side Information

Jin Lu
Guannan Liang
Jiangwen Sun
Jinbo Bi

Matrix completion methods can benefit from side information besides the partially observed matrix. The use of side features describing the row and column entities of a matrix has been shown to reduce the sample complexity for completing the matrix. We propose a novel sparse formulation that explicitly models the interaction between the row and column side features to approximate the matrix entries. Unlike early methods, this model does not require the low-rank condition on the model parameter matrix. We prove that when the side features can span the latent feature space of the matrix to be recovered, the number of observed entries needed for an exact recovery is $O(\log N)$ where $N$ is the size of the matrix. When the side features are corrupted latent features of the matrix with a small perturbation, our method can achieve an $\epsilon$-recovery with $O(\log N)$ sample complexity, and maintains a $\O(N^{3/2})$ rate similar to classfic methods with no side information. An efficient linearized Lagrangian algorithm is developed with a strong guarantee of convergence. Empirical results show that our approach outperforms three state-of-the-art methods both in simulations and on real world datasets.

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JMLR Journal 2016 Journal Article

Multiplicative Multitask Feature Learning

Xin Wang
Jinbo Bi
Shipeng Yu
Jiangwen Sun
Minghu Song

We investigate a general framework of multiplicative multitask feature learning which decomposes individual task's model parameters into a multiplication of two components. One of the components is used across all tasks and the other component is task-specific. Several previous methods can be proved to be special cases of our framework. We study the theoretical properties of this framework when different regularization conditions are applied to the two decomposed components. We prove that this framework is mathematically equivalent to the widely used multitask feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers. Further, an analytical formula is derived for the across-task component as related to the task- specific component for all these regularizers, leading to a better understanding of the shrinkage effects of different regularizers. Study of this framework motivates new multitask learning algorithms. We propose two new learning formulations by varying the parameters in the proposed framework. An efficient blockwise coordinate descent algorithm is developed suitable for solving the entire family of formulations with rigorous convergence analysis. Simulation studies have identified the statistical properties of data that would be in favor of the new formulations. Extensive empirical studies on various classification and regression benchmark data sets have revealed the relative advantages of the two new formulations by comparing with the state of the art, which provides instructive insights into the feature learning problem with multiple tasks. [abs] [ pdf ][ bib ] &copy JMLR 2016. ( edit, beta )

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ICML Conference 2015 Conference Paper

Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization

Jiangwen Sun
Jin Lu 0001
Tingyang Xu
Jinbo Bi

When multiple views of data are available for a set of subjects, co-clustering aims to identify subject clusters that agree across the different views. We explore the problem of co-clustering when the underlying clusters exist in different subspaces of each view. We propose a proximal alternating linearized minimization algorithm that simultaneously decomposes multiple data matrices into sparse row and columns vectors. This approach is able to group subjects consistently across the views and simultaneously identify the subset of features in each view that are associated with the clusters. The proposed algorithm can globally converge to a critical point of the problem. A simulation study validates that the proposed algorithm can identify the hypothesized clusters and their associated features. Comparison with several latest multi-view co-clustering methods on benchmark datasets demonstrates the superior performance of the proposed approach.

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JBHI Journal 2014 Journal Article

Multiview Comodeling to Improve Subtyping and Genetic Association of Complex Diseases

Jiangwen Sun
Jinbo Bi
Henry R. Kranzler

Genetic association analysis of complex diseases has been limited by heterogeneity in their clinical manifestations and genetic etiology. Research has made it possible to differentiate homogeneous subtypes of the disease phenotype. Currently, the most sophisticated subtyping methods perform unsupervised cluster analysis using only clinical features of a disorder, resulting in subtypes for which genetic association may be limited. In this study, we seek to derive a novel multiview data analytic method that integrates two views of the data: the clinical features and the genetic markers of the same set of patients. Our method is based on multiobjective programming that is capable of clinically categorizing a disease phenotype so as to discover genetically different subtypes. We optimize two objectives jointly: 1) in cluster analysis, the derived clusters should differ significantly in clinical features; 2) these clusters can be well separated using genetic markers by constructed classifiers. Extensive computational experiments with two substance-use disorders using two populations show that the proposed algorithm is superior to existing subtyping methods.

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NeurIPS Conference 2014 Conference Paper

On Multiplicative Multitask Feature Learning

Xin Wang
Jinbo Bi
Shipeng Yu
Jiangwen Sun

We investigate a general framework of multiplicative multitask feature learning which decomposes each task's model parameters into a multiplication of two components. One of the components is used across all tasks and the other component is task-specific. Several previous methods have been proposed as special cases of our framework. We study the theoretical properties of this framework when different regularization conditions are applied to the two decomposed components. We prove that this framework is mathematically equivalent to the widely used multitask feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers. Further, an analytical formula is derived for the across-task component as related to the task-specific component for all these regularizers, leading to a better understanding of the shrinkage effect. Study of this framework motivates new multitask learning algorithms. We propose two new learning formulations by varying the parameters in the proposed framework. Empirical studies have revealed the relative advantages of the two new formulations by comparing with the state of the art, which provides instructive insights into the feature learning problem with multiple tasks.

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TIST Journal 2013 Journal Article

A machine learning approach to college drinking prediction and risk factor identification

Jinbo Bi
Jiangwen Sun
Yu Wu
Howard Tennen
Stephen Armeli

Alcohol misuse is one of the most serious public health problems facing adolescents and young adults in the United States. National statistics shows that nearly 90% of alcohol consumed by youth under 21 years of age involves binge drinking and 44% of college students engage in high-risk drinking activities. Conventional alcohol intervention programs, which aim at installing either an alcohol reduction norm or prohibition against underage drinking, have yielded little progress in controlling college binge drinking over the years. Existing alcohol studies are deductive where data are collected to investigate a psychological/behavioral hypothesis, and statistical analysis is applied to the data to confirm the hypothesis. Due to this confirmatory manner of analysis, the resulting statistical models are cohort-specific and typically fail to replicate on a different sample. This article presents two machine learning approaches for a secondary analysis of longitudinal data collected in college alcohol studies sponsored by the National Institute on Alcohol Abuse and Alcoholism. Our approach aims to discover knowledge, from multiwave cohort-sequential daily data, which may or may not align with the original hypothesis but quantifies predictive models with higher likelihood to generalize to new samples. We first propose a so-called temporally-correlated support vector machine to construct a classifier as a function of daily moods, stress, and drinking expectancies to distinguish days with nighttime binge drinking from days without for individual students. We then propose a combination of cluster analysis and feature selection, where cluster analysis is used to identify drinking patterns based on averaged daily drinking behavior and feature selection is used to identify risk factors associated with each pattern. We evaluate our methods on two cohorts of 530 total college students recruited during the Spring and Fall semesters, respectively. Cross validation on these two cohorts and further on 100 random partitions of the total students demonstrate that our methods improve the model generalizability in comparison with traditional multilevel logistic regression. The discovered risk factors and the interaction of these factors delineated in our models can set a potential basis and offer insights to a new design of more effective college alcohol interventions.

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