Title :
Constrained principal component analysis via an orthogonal learning network
Author_Institution :
Princeton Univ., NJ, USA
Abstract :
The regular principal components (PC) analysis of stochastic processes is extended to the so-called constrained principal components (CPC) problem. The CPC analysis involves extracting representative components which contain the most information about the original processes, The CPC solution has to be extracted from a given constraint subspace. Therefore, the CPC solution may be adopted to best recover the original signal and simultaneously avoid the undesirably noisy or redundant components. A technique for finding optimal CPC solutions via an orthogonal learning network (OLN) is proposed. The underlying numerical analysis for the theoretical proof of the convergency of OLN is discussed. The same numerical analysis provides a useful estimate of optimal learning rates leading to very fast convergence speed. Simulation and application examples are provided
Keywords :
learning systems; neural nets; stochastic processes; analysis of stochastic processes; constrained principal component analysis; estimate of optimal learning rates; fast convergence speed; orthogonal learning network; Data compression; Data mining; Equations; Information analysis; Motion analysis; Neurons; Numerical analysis; Principal component analysis; Stochastic processes; Subspace constraints;
Conference_Titel :
Circuits and Systems, 1990., IEEE International Symposium on
Conference_Location :
New Orleans, LA
DOI :
10.1109/ISCAS.1990.112180