Sparse PCA via Hard Thresholding for Blind Source Separation
Journal
IEEE International Conference on Acoustics, Speech, and Signal Processing
Date Issued
2016-03
Author(s)
M.C. Wu
Abstract
Principal Component Analysis (PCA) is adopted in diverse areas including signal processing and machine leaning. However, the derived principal components, the linear combinations of the original variables, are hard to be interpreted in many applications especially the blind source separation. Therefore, we propose regularized PCA via hard thresholding such that the derived loadings are sparse and easier to be interpreted. The proposed method has advantages due to the adoption of hard thresholding. First, the proposed method can be implemented by linear operators and thus computational efficient even in p ≫ n or large p scenarios. Second, the threshold can be objectively selected based on statistical decision theory without domain knowledge. Moreover, simulations show the superiority of our method compared to the L 1 -penalized method. Therefore, our approach can be a strong competitor of the existing sparse PCA.
SDGs
Type
conference paper