Digital image processing
A new adaptive algorithm for minor component analysis
Signal Processing
On a Class of Orthonormal Algorithms for Principal and Minor Subspace Tracking
Journal of VLSI Signal Processing Systems
Projection approximation subspace tracking
IEEE Transactions on Signal Processing
Global convergence of Oja's subspace algorithm for principal component extraction
IEEE Transactions on Neural Networks
Multidimensional Systems and Signal Processing
Multidimensional Systems and Signal Processing
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This paper presents a new adaptive minor subspace extraction algorithm based on an idea of Peng and Yi ('07) for approximating the single minor eigenvector of a covariance matrix. By utilizing the idea inductively in the nested orthogonal complement subspaces, the proposed algorithm succeeds to relax the numerical sensitivity which has been annoying conventional adaptive minor subspace extraction algorithms for example, Oja algorithm ('82) and its stabilized version: O-Oja algorithm ('02). Simulation results demonstrate that the proposed algorithm realizes more stable convergence than O-Oja algorithm.