Author name cluster

Guoping Long

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3 papers

2 author rows

IJCAI Conference 2017 Conference Paper

Nonlinear Maximum Margin Multi-View Learning with Adaptive Kernel

Jia He
Changying Du
Changde Du
Fuzhen Zhuang
Qing He
Guoping Long

Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning. Apart from the huge consumption of manpower, computation and memory resources, most of these models seek point estimation of their parameters, and are prone to overfitting to small training data. This paper presents an adaptive kernel nonlinear max-margin multi-view learning model under the Bayesian framework. Specifically, we regularize the posterior of an efficient multi-view latent variable model by explicitly mapping the latent representations extracted from multiple data views to a random Fourier feature space where max-margin classification constraints are imposed. Assuming these random features are drawn from Dirichlet process Gaussian mixtures, we can adaptively learn shift-invariant kernels from data according to Bochners theorem. For inference, we employ the data augmentation idea for hinge loss, and design an efficient gradient-based MCMC sampler in the augmented space. Having no need to compute the Gram matrix, our algorithm scales linearly with the size of training set. Extensive experiments on real-world datasets demonstrate that our method has superior performance.

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IJCAI Conference 2016 Conference Paper

Online Bayesian Max-Margin Subspace Multi-View Learning

Jia He
Changying Du
Fuzhen Zhuang
Xin Yin
Qing He
Guoping Long

Last decades have witnessed a number of studies devoted to multi-view learning algorithms, however, few efforts have been made to handle online multi-view learning scenarios. In this paper, we propose an online Bayesian multi-view learning algorithm to learn predictive subspace with max-margin principle. Specifically, we first define the latent margin loss for classification in the subspace, and then cast the learning problem into a variational Bayesian framework by exploiting the pseudo-likelihood and data augmentation idea. With the variational approximate posterior inferred from the past samples, we can naturally combine historical knowledge with new arrival data, in a Bayesian Passive-Aggressive style. Experiments on various classification tasks show that our model have superior performance.

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UAI Conference 2016 Conference Paper

Online Bayesian Multiple Kernel Bipartite Ranking

Changying Du
Changde Du
Guoping Long
Qing He 0003
Yucheng Li 0002

Bipartite ranking aims to maximize the area under the ROC curve (AUC) of a decision function. To tackle this problem when the data appears sequentially, existing online AUC maximization methods focus on seeking a point estimate of the decision function in a linear or predefined single kernel space, and cannot learn effective kernels automatically from the streaming data. In this paper, we first develop a Bayesian multiple kernel bipartite ranking model, which circumvents the kernel selection problem by estimating a posterior distribution over the model weights. To make our model applicable to streaming data, we then present a kernelized online Bayesian passive-aggressive learning framework by maintaining a variational approximation to the posterior based on data augmentation. Furthermore, to efficiently deal with large-scale data, we design a fixed budget strategy which can effectively control online model complexity. Extensive experimental studies confirm the superiority of our Bayesian multi-kernel approach.

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