[Show abstract][Hide abstract] ABSTRACT: Data with high dimensionality often occurs, which will produce large time and energy overheads when directly used in classification tasks. So, as one of the most important fields in machine learning, dimensionality reduction has been paid more and more attention and has achieved a prodigious progress in the theory and algorithm research. Linear Graph embedding (LGE) model is an efficient tool for dimensionality reduction. According to the problems of supervised dimensionality reduction with Non-Gaussian data distributions and at the same time consider neighborhood preserving relations among samples, a novel subspace learning method, neighborhood preserving and marginal discriminant embedding (NP-MDE), is proposed based on LGE and marginal Fisher analysis in this paper. NP-MDE could minimize the within-class scatter and meanwhile maximize the margin among different classes. Moreover, the neighborhood structure with each class is preserved. Experiments on Yale face image data sets show that after dimensionality reduction using NP-MDE, the average classification accuracy is very good.
[Show abstract][Hide abstract] ABSTRACT: With an intensive study of the existing density-sensitive distance measures, we proposed a new distance measure for graph-based semi-supervised learning. The proposed measure can not only effectively amplify the distance between data points in different high-density regions, but also reduce the distance among data points in a same high-density region. Then, a graph-based semi-supervised clustering algorithm is presented based on the proposed distance measure. Experimental results on some UCI data sets show that the proposed method has obvious advantages than the old one.
Machine Learning and Cybernetics (ICMLC), 2011 International Conference on; 08/2011