title: Large-margin predictive latent subspace learning for multiview data analysis.
abstract: Learning salient representations of multiview data is an essential step in many applications such as image classification, retrieval, and annotation. Standard predictive methods, such as support vector machines, often directly use all the features available without taking into consideration the presence of distinct views and the resultant view dependencies, coherence, and complementarity that offer key insights to the semantics of the data, and are therefore offering weak performance and are incapable of supporting view-level analysis. This paper presents a statistical method to learn a predictive subspace representation underlying multiple views, leveraging both multiview dependencies and availability of supervising side-information. Our approach is based on a multiview latent subspace Markov network (MN) which fulfills a weak conditional independence assumption that multiview observations and response variables are conditionally independent given a set of latent variables. To learn the latent subspace MN, we develop a large-margin approach which jointly maximizes data likelihood and minimizes a prediction loss on training data. Learning and inference are efficiently done with a contrastive divergence method. Finally, we extensively evaluate the large-margin latent MN on real image and hotel review datasets for classification, regression, image annotation, and retrieval. Our results demonstrate that the large-margin approach can achieve significant improvements in terms of prediction performance and discovering predictive latent subspace representations.
authors.affiliation: Department of Computer Science and Technology, Tsinghua National Laboratory of Information Science and Technology, State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, FIT Building, Haidian District, Beijing 100084, China. ningchen@tsinghua.edu.cn
authors.forename: Ning
authors.initials: N
authors.lastname: Chen
authors.forename: Jun
authors.initials: J
authors.lastname: Zhu
authors.forename: Fuchun
authors.initials: F
authors.lastname: Sun
authors.forename: Eric Poe
authors.initials: EP
authors.lastname: Xing