Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized low rank approximations of matrices
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The equivalence of two-dimensional PCA to line-based PCA
Pattern Recognition Letters
Matrix-pattern-oriented least squares support vector classifier with AdaBoost
Pattern Recognition Letters
Generalized low-rank approximations of matrices revisited
IEEE Transactions on Neural Networks
Texture descriptors for generic pattern classification problems
Expert Systems with Applications: An International Journal
Matrix representation in pattern classification
Expert Systems with Applications: An International Journal
Comparing and combining spatial dimension reduction methods in face verification
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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As an extension to 2DPCA, generalized low rank approximation of matrices (GLRAM) applies two-sided (i.e., the left and right) rather than single-sided (i.e., the left or the right alone) linear projecting transform(s) to each 2D image for compression and feature extraction. Its advantages over 2DPCA include higher compression ratio, superior classification performance, etc. However, GLRAM can only adopt an iterative rather than analytical approach to get the left and right projecting transforms and lacks a criterion to automatically determine the dimensionality of the projected matrix. In this paper, a novel non-iterative GLRAM (NIGLRAM) is proposed to overcome the above shortcomings. Experimental results on ORL and AR face datasets and COIL-20 object dataset show that NIGLRAM can get not only so-needed closed-form transforms but also comparable performance to GLRAM.