Multi-view learning via probabilistic latent semantic analysis

Fuzhen Zhuang, George Karypis, Xia Ning, Qing He, and Zhongzhi Shi
Information Sciences, 199; pp. 20-30, 2012
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Multi-view learning arouses vast amount of interest in the past decades with numerous real-world applications in web page analysis, bioinformatics, image processing and so on. Unlike the most previous works following the idea of co-training, in this paper we propose a new generative model for Multi-view Learning via Probabilistic Latent Semantic Analysis, called MVPLSA. In this model, we jointly model the co-occurrences of features and documents from different views. Specifically, in the model there are two latent variables y for the latent topic and z for the document cluster, and three visible variables d for the document, f for the feature, and v for the view label. The conditional probability p(z?d), which is independent of v, is used as the bridge to share knowledge among multiple views. Also, we have p(y?z, v) and p(f?y, v), which are dependent of v, to capture the specifical structures inside each view. Experiments are conducted on four real-world data sets to demonstrate the effectiveness and superiority of our model.
Research topics: Classification | Data mining