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|Title:||Principal component analysis based on non-parametric maximum entropy||Authors:||He, R.
Information theoretic learning
|Issue Date:||Jun-2010||Citation:||He, R., Hu, B., Yuan, X., Zheng, W.-S. (2010-06). Principal component analysis based on non-parametric maximum entropy. Neurocomputing 73 (10-12) : 1840-1852. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neucom.2009.12.032||Abstract:||In this paper, we propose an improved principal component analysis based on maximum entropy (MaxEnt) preservation, called MaxEnt-PCA, which is derived from a Parzen window estimation of Renyi's quadratic entropy. Instead of minimizing the reconstruction error either based on L2-norm or L1-norm, the MaxEnt-PCA attempts to preserve as much as possible the uncertainty information of the data measured by entropy. The optimal solution of MaxEnt-PCA consists of the eigenvectors of a Laplacian probability matrix corresponding to the MaxEnt distribution. MaxEnt-PCA (1) is rotation invariant, (2) is free from any distribution assumption, and (3) is robust to outliers. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed linear method as compared to other related robust PCA methods. © 2010 Elsevier B.V.||Source Title:||Neurocomputing||URI:||http://scholarbank.nus.edu.sg/handle/10635/82929||ISSN:||09252312||DOI:||10.1016/j.neucom.2009.12.032|
|Appears in Collections:||Staff Publications|
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