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This book shows how constrained principal component analysis (CPCA) offers a unified framework for regression techniques and PCA. Keeping the use of complicated iterative methods to a minimum, the book includes implementation details and many real application examples. It also offers material for methodologically oriented readers interested in d
This valuable reference on projectors, generalized inverses, and SVD covers concepts numerous cutting-edge concepts and provides systematic and in-depth accounts of these ideas from the viewpoint of linear transformations of finite dimensional vector spaces.
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