IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Framework for Subspace Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subclass Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Incremental Linear Discriminant Analysis for Face Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental Kernel Principal Component Analysis
IEEE Transactions on Image Processing
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
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In subspace approaches for the pattern recognition, the transform fashion is paid more attentions but the correlation between subspaces is given little concern in previous research. By mapping the space of all training samples to the corresponding subspace of individual training sample using PCA, we discover that there is a very strong relationship between two subspaces. Specially, a higher mutual compensability and consistency appears in both of these two subspaces. Therefore, a new recognition algorithm based on the difference of double subspaces is presented in this paper. The new algorithm sufficiently utilizes the relativity of PCA Eigen-subspaces of the total sample and individual sample spaces of the sample to be recognized, so that it improve efficiently the recognition rate. We prove the validity of the proposed algorithm under some mild divisible condition, and give some the experiments to demonstrate that the new algorithm has higher recognition rate than some similar algorithms.