Abstract
Document analysis plays an important role in our life, and traditional models like Latent Semantic Analysis (LSI) or Latent Dirichlet Allocation (LDA) cannot handle data from many sources. Multi-view learning technology like Multi-view Intact Space Learning (MISL), which integrates the complementary information on multiple views to discover a latent intact representation of the data, is effective for image or video application. But the model has not been applied to multi-lingual documents and has not considered the intrinsic geometrical and discriminating structure of the document data. To overcome this issue, we assume that if documents are close in the origin representation, they should also be close in the intact space representation. And we introduce a manifold regularization term to MISL so that the data is more smoothly in latent space. We conduct classification experiments on 10505 Wiki documents we crawled, and the result shows that it is outperforming TFIDF, LSI, LDA, and MISL.
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Zhan, Z., Ma, Z. (2017). Document Analysis Based on Multi-view Intact Space Learning with Manifold Regularization. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_4
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DOI: https://doi.org/10.1007/978-3-319-67777-4_4
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