Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Mixtures of probabilistic principal component analyzers
Neural Computation
A constrained EM algorithm for principal component analysis
Neural Computation
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
An Expectation-Maximization Approach to Nonlinear Component Analysis
Neural Computation
| Hi-index | 0.01 |
In this note, from another point of view and in a more general situation, we formulate an EM algorithm for finding the leading eigen-system of any positive semi-definite matrix in a very simple derivation. The proposed EM approach can directly compute not only the eigen-system of sample covariance matrix in data space but also that of kernel matrix. Thus, the proposed algorithm provides an unified framework for EM-based principal component analysis (PCA) and kernel PCA (KPCA). Particularly, when it is applied to KPCA, it is a dual form of the commonly used constrained EM algorithm for performing KPCA. And thus it is a beneficial complementarity or dual description of the constrained EM method.