A ZGPCA Algorithm for Subspace Estimation
Haoran Yi, Deepu Rajan, Liang-Tien Chia · 2007
We propose a new algorithm called the ZGPCA algorithm for subspace estimation based on the GPCA (Generalized Principal Component Analysis) algorithm. It is formulated within an FIR filter framework so that the norm vectors of the subspaces correspond to filter coefficients. It is shown that such an approach leads to a more accurate and computationally efficient method compared to the GPCA algorithm. We extend the ZGPCA algorithm to make it recursive so that subspaces with possibly different dimensions can be obtained. We also propose a new distance measure that can be used for k-means clustering of sample points within a subspace. Experimental results on synthetic data and applications on face clustering and sports video clustering show good performance of the proposed algorithm.