An Analysis of Linear Subspace Approaches for Computer Vision and Pattern Recognition

: Linear subspace analysis (LSA) has become rather ubiquitous in a wide range of problems arising in pattern recognition and computer vision. The essence of these approaches is that certain structures are intrinsically (or approximately) low dimensional: for example, the factorization approach to the problem of structure from motion (SFM) and principal component analysis (PCA) based approach to face recognition. In LSA, the singular value decomposition (SVD) is usually the basic mathematical tool. However, analysis of the performance, in the presence of noise, has been lacking. We present such an analysis here. First, the "denoising capacity" of the SVD is analysed. Specifically, given a rank-r matrix, corrupted by noise

• Type: Journal
• Year: 2006
• Where: IJCV
• Authors: Pei Chen, David Suter
• Keywords: Analysis, IJCV 2006, Singular Value Decomposition, True Subspace