Constrained principal component extraction network

Constrained principal component (CPC) analysis of stochastic process extracts the most representative components from a given constraint subspace. It is an effective means to incorporate external information into principal component analysis (PCA) and is appealing in a variety of application areas. This paper proposes a novel autoassociative network to find optimal CPC solutions and compares the proposed method with Kungpsilas orthogonal learning network (OLN) approach. As a complement, its relationship with other existing techniques and possible extensions are also discussed.