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Face recognition using Principal Component Analysis
Facial recognition can be done by various methods such as principal component analysis (PCA), linear discriminant analysis (LDA), independent component analysis (ICA), a local binary pattern ...
Independent Component Analysis edited by Stephen Roberts
Independent Component Analysis (ICA) has recently become an important tool for modelling and understanding empirical datasets. It is a method of separating out independent sources from linearly mixed data, and belongs to the class of general linear models.
A Multifactor Extension of Linear Discriminant Analysis ...
Linear Discriminant Analysis (LDA) and Multilinear Principal Component Analysis (MPCA) are leading subspace methods for achieving dimension reduction based on supervised learning. Both LDA and MPCA use class labels of data samples to calculate subspaces onto which these samples are projected. Furthermore, both methods have been successfully applied to face recognition....
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