Performance of robust two-dimensional principal component for classification

Abstract

The robust dimension reduction for classification of two dimensional data is discussed in this paper. The classification process is done with reference of original data. The classifying of class membership is not easy when more than one variable are loaded with the same information, and they can be written as a near linear combination of other variables. The standard approach to overcome this problem is dimension reduction. One of the most common forms of dimensionality reduction is the principal component analysis (PCA). The two-dimensional principal component (2DPCA) is often called a variant of principal component. The image matrices were directly treated as 2D matrices; the covariance matrix of image can be constructed directly using the original image matrices. The presence of outliers in the data has been proved to pose a serious problem in dimension reduction. The first component consisting of the greatest variation is often pushed toward the anomalous observations. The robust minimizing vector variance (MW) combined with two dimensional projection approach is used for solving the problem. The computation experiment shows the robust method has the good performances for matrix data classification.