Block Principle Component Analysis with Lp-norm for Robust and Sparse Modelling

Author:

Tang Ganyi,Lu Guifu

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference18 articles.

1. JOLLIFFE I T. Principal component analysis [M]. New York, USA: Springer, 2002.

2. YANG J, ZHANG D, FRANGI A F, et al. Twodimensional PCA: A new approach to appearancebased face representation and recognition [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131–137.

3. KE Q F, KANADE T. Robust L1-norm factorization in the presence of outliers and missing data by alternative convex programming [C]//Proceedings of the 2005 Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE, 2005: 739–746.

4. DING C, ZHOU D, HE X F, et al. R1-PCA: Rotational invariant L1-norm principal component analysis for robust subspace factorization [C]//Proceedings of the 23rd International Conference on Machine Learning. Pittsburgh, USA: ACM, 2006: 281–288.

5. KWAK N. Principal component analysis based on L1-norm maximization [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2008, 30(9): 1672–1680.

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