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Published in:
2013 | OriginalPaper | Chapter
Non-negative Mutative-Sparseness Coding towards Hierarchical Representation
Authors : Jiayun Cao, Lanjuan Zhu
Published in: Intelligent Computing for Sustainable Energy and Environment
Publisher: Springer Berlin Heidelberg
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In this work, the problem of data representation is studied. Many existing works focus on how to learn a set of bases, sparsely and effectively representing the data. However, as reaching a consensus, people always arrange the data in the hierarchical structure so that a clearer data framework and interaction can be got, just like patent documents listed by different layers of classes. Thus, different from existing works, we target at discovering the hierarchical representation of data. Non-negative mutative-sparseness coding (NMSC) is a method for analyzing the non-negative sparse components of multivariate data and representing the data as hierarchical structure. Specifically in a subsequent layer, the sparseness of each data is adjusted according to the corresponding hidden components in the upper layer. Our experimental evaluations show that the NMSC possesses great efficiency in clustering and sufficient merit in hierarchical organizing the observed document data.