Abstract
In principal component analysis (PCA), ℓ2 /ℓ1-norm is widely used to measure coding residual. In this case, it assume that the residual follows Gaussian/Laplacian distribution. However, it may fail to describe the coding errors in practice when there are outliers. Toward this end, this paper propose a Robust Sparse PCA (RSPCA) approach to solve the outlier problem, by modeling the sparse coding as a sparsity-constrained weighted regression problem. By using a series of equivalent transformations, we show the proposed RSPCA is equivalent to the Weighted Elastic Net (WEN) problem and thus the Least Angle Regression Elastic Net (LARS-EN) algorithm is used to yield the optimal solution. Simulation results illustrated the effectiveness of this approach.
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Wang, L., Cheng, H. (2012). Robust Sparse PCA via Weighted Elastic Net. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_12
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DOI: https://doi.org/10.1007/978-3-642-33506-8_12
Publisher Name: Springer, Berlin, Heidelberg
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