M. Prakash et Mn. Murty, A GENETIC APPROACH FOR SELECTION OF (NEAR) OPTIMAL SUBSETS OF PRINCIPAL COMPONENTS FOR DISCRIMINATION, Pattern recognition letters, 16(8), 1995, pp. 781-787
Citations number
19
Categorie Soggetti
Computer Sciences, Special Topics","Computer Science Artificial Intelligence
Principal Component Analysis (PCA) is being used both in the preproces
sor to a feed-forward neural network and in the Subspace Pattern Recog
nition Method (SPRM). Most of the classifiers based on PCA use the fir
st few Principal Components (PCs) associated with dominant eigenvalues
of the pattern covariance matrix. Recent investigations reveal that c
onsidering PCs corresponding to the non-dominant eigenvalues will resu
lt in the design of better classifiers in certain application domains:
the PCs that are most useful for discrimination may fall in the entir
e spectrum of PCs. Finding an optimal subset of PCs which maximizes th
e classification rate of a selected classifier is computationally expe
nsive as the search space increases exponentially with the increase in
either the dimensionality or the number of classes. In this paper, an
approach based on genetic algorithms is proposed to search for an opt
imal subset of PCs. SPRM is used as the classifier because of its comp
utational simplicity. The proposed approach is tested on two real data
sets, and the results obtained are better than those obtained with Cl
afic, an algorithm used to choose the basis vectors in the SPRM. Altho
ugh the PCs selected were from the entire spectrum, a considerable red
uction in dimensionality was still present.