A novel pattern recognition technique based on group clustering computing and convex optimization for dimensionality reduction

Abstract

In the field of pattern recognition and data learning process the dimensionality reduction is a necessary method for providing a personalized data in perfect numbers. The increase in research proclaims the importance of data reduction in current trends. The old traditional reduction techniques use ranking systems. But this differs from others in reduction of data without lagging its performance in terms of increased efficiency and less percentage of error occurrences. The area of dimensionality reduction not only ends in pattern recognition and also in many high dimensional data processing elements such as text categorization, indexing of documents and mainly in gene expression data. The feature extraction and feature selection are the two steps of process in reduction. The process proposed is statistical pattern recognition process and a paradigm for this approach to the problem is summarized herein. The four data process includes is (1) evaluation (2) acquisition (3) feature selection, and (4) statistical model of feature selection. We show how to reduce the dimensionality by utilizing group and convexity model. This paper concluded by an integrated approach to the implementation dimensionality reduction process with an effort to maximize the efficiency and accuracy.