EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Mixtures of probabilistic principal component analyzers
Neural Computation
An Expectation-Maximization Approach to Nonlinear Component Analysis
Neural Computation
On Convergence Conditions of an Extended Projection Neural Network
Neural Computation
Constrained Projection Approximation Algorithms for Principal Component Analysis
Neural Processing Letters
Letters: An unified EM algorithm for PCA and KPCA
Neurocomputing
Statistics and Computing
On variations of power iteration
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
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We propose a constrained EM algorithm for principal component analysis (PCA) using a coupled probability model derived from single-standard factor analysis models with isotropic noise structure. The single probabilistic PCA, especially for the case where there is no noise, can find only a vector set that is a linear superposition of principal components and requires postprocessing, such as diagonalization of symmetric matrices. By contrast, the proposed algorithm finds the actual principal components, which are sorted in descending order of eigenvalue size and require no additional calculation or postprocessing. The method is easily applied to kernel PCA. It is also shown that the new EM algorithm is derived from a generalized least-squares formulation.