2015 IEEE Winter Conference on Applications of Computer Vision (WACV) (2015)
Waikoloa, HI, USA
Jan. 5, 2015 to Jan. 9, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WACV.2015.78
Latent Low-Rank Representation (Lat LRR) has the empirical capability of identifying "salient" features. However, the reason behind this feature extraction effect is still not understood. Its optimization leads to non-unique solutions and has high computational complexity, limiting its potential in practice. We show that Lat LRR learns a transformation matrix which suppresses the most significant principal components corresponding to the largest singular values while preserving the details captured by the components with relatively smaller singular values. Based on this, we propose a novel feature extraction method which directly designs the transformation matrix and has similar behavior to Lat LRR. Our method has a simple analytical solution and can achieve better performance with little computational cost. The effectiveness and efficiency of our method are validated on two face recognition datasets.
Feature extraction, Approximation methods, Optimization, Algorithm design and analysis, Noise, Approximation algorithms, Principal component analysis
Y. Wang, V. I. Morariu and L. S. Davis, "Unsupervised Feature Extraction Inspired by Latent Low-Rank Representation," 2015 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2015, pp. 542-549.