A modular eigen subspace scheme for high-dimensional data classification
In this paper, a novel filter-based greedy modular subspace (GMS) technique is proposed to improve the accuracy of high-dimensional data classification. The proposed approach initially divides the whole set of high-dimensional features into several arbitrary number of highly correlated subgroups by performing a greedy correlation matrix reordering transformation for each class. These GMS can be treated as not only a preprocess of GMS filter-based classifiers but also a unique feature extractor to generate a particular feature subspaces for each different class presented in high-dimensional data. The similarity measures are next calculated by projecting the samples into different modular feature subspaces. Finally, a GMS filter-based architecture based on the mean absolute errors criterion is adopted to build a non-linear multi-class classifier. The proposed GMS filter-based classification scheme is developed to find non-linear boundaries of different classes for high-dimensional data. It not only significantly improves the classification accuracy but also dramatically reduces the computational complexity of feature extraction compared with the conventional principal components analysis. Experimental results demonstrate that the proposed GMS feature extraction method suits the GMS filter-based classifier best as a classification preprocess. It significantly improves the precision of high-dimensional data classification.
high-dimensional data classification greedy modular subspaces mean absolute errors principal components analysis