Matrix computations (3rd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Updating Problems in Latent Semantic Indexing
SIAM Journal on Scientific Computing
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IDR/QR: An Incremental Dimension Reduction Algorithm via QR Decomposition
IEEE Transactions on Knowledge and Data Engineering
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
The Journal of Machine Learning Research
Neighborhood MinMax projections
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Fast algorithm for updating the discriminant vectors of dual-space LDA
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Neural Networks
Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications
International Journal of Computer Vision
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental Linear Discriminant Analysis for Face Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An optimization criterion for generalized discriminant analysis on undersampled problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The complete linear discriminant analysis (CLDA) algorithm has been proven to be an effective tool for face recognition. The CLDA method can make full use of the discriminant information of the training samples. However, the original implementation of CLDA may not suitable for incremental learning problem. In this paper, we first propose a new implementation of CLDA, which is theoretically equivalent to the original implementation of CLDA but is more efficient than the original one. Then, based on our proposed novel implementation of CLDA, we propose the incremental CLDA method which can accurately update the discriminant vectors of CLDA when new samples are inserted into the training set. Experiments on ORL, AR and PIE face databases show the efficiency of our proposed CLDA algorithms over the original implementation of CLDA.