HEBBIAN LEARNING SUBSPACE METHOD - A NEW APPROACH

In this paper, we propose a new learning algorithm for the Subspace Pa ttern Recognition Method (SPRM) called the Hebbian Learning Subspace M ethod (HLSM). It uses the notion of a weighted squared orthogonal proj ection distance which gives different weightages to different basis ve ctors in the computation of the orthogonal projection distance. The pr inciple applied during learning is the same as that used in the earlie r Learning Subspace Method (LSM): the projection on the wrong subspace is always decreased and the one on the correct subspace is always inc reased. We also propose a neural implementation for the HLSM. Experime nts have been conducted on an extensive numeric set of handprinted cha racters involving 16659 samples using the SPRM, the HLSM and the Avera ged LSM. Excellent results have been obtained using all the subspace m ethods thus demonstrating the suitability of subspace methods for this application. Copyright (C) 1996 Pattern Recognition Society.