M. Prakash et Mn. Murty, GROWING SUBSPACE PATTERN-RECOGNITION METHODS AND THEIR NEURAL-NETWORKMODELS, IEEE transactions on neural networks, 8(1), 1997, pp. 161-168
In statistical pattern recognition, the decision of which features to
use is usually left to human judgment. If possible, automatic methods
are desirable. Like multilayer perceptrons, learning subspace methods
(LSM's) have the potential to integrate feature extraction and classif
ication. In this paper, we propose two new algorithms, along with thei
r neural-network implementations, to overcome certain limitations of t
he earlier LSM's. By introducing one cluster at a time and adapting it
if necessary, we eliminate one limitation of deciding how many cluste
rs to have in each class by trial-and-error. By using the principal co
mponent analysis neural networks along with this strategy, we propose
neural-network models which are better in overcoming another limitatio
n, scalability. Our results indicate that the proposed classifiers are
comparable to classifiers like the multilayer perceptrons and the nea
rest-neighbor classifier in terms of classification accuracy. In terms
of classification speed and scalability in design, they appear to be
better for large-dimensional problems.