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Since similarity plays a key role for both clustering and classification purposes, the problem of finding a relevant indicators to measure the similarity between two patterns drawn from the same feature space became of major importance. The advantages of using principal components reside from the fact that bands are uncorrelated and no information contained in one band can be predicted by the knowledge of the other bands. The semi-supervised learning (SSL) problem has recently drawn large attention in the machine learning community, mainly due to its significant importance in practical applications. The aims of the research reported in this paper are to report experimentally derived conclusions on the performance of a PCA-based supervised technique in a semi-supervised environment. A series of conclusions experimentally established by tests performed on samples of signals coming from two classes are exposed in the final section of the paper.

TOWARD A SEMI-SUPERVISED APPROACH IN CLASSIFICATION BASED ON PRINCIPAL DIRECTIONS