Abstract
In this paper, a novel method is proposed for dimensionality reduction of proportional data. Non-negative, unit-sum data, namely, proportional data emerges in many applications such as document classification, image classification using visual bag of words, etc. The introduced method is supervised and can be used for classification of data into binary classes. In the proposed method, the intra-class correlation is maximized while minimizing the interclass correlation, using a linear transform. Design of this transform is formulated as an optimization problem with proper cost function. The projected data is matched to two Dirichlet distributions with careful parameter selection which allows to separate the classes in the Dirichlet parameter space. Finally, simulations are performed to demonstrate the effectiveness of the algorithm.
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Masoudimansour, W., Bouguila, N. (2015). Dimensionality Reduction of Proportional Data Through Data Separation Using Dirichlet Distribution. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_15
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DOI: https://doi.org/10.1007/978-3-319-20801-5_15
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