Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Semi-supervised sub-manifold discriminant analysis
Pattern Recognition Letters
Linear dimensionality reduction using relevance weighted LDA
Pattern Recognition
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Most existing typical dimension reduction methods, for example Isomap algorithm, are hard to deal with the problem of violation of pairwise constraint. In this paper, a pairwise-constraint supervised Isomap algorithm (PC-SIsomap) is proposed, in which the supervised information is taken on the form of pairwise constraint introduced to geodesic distance. Mapping high-dimensional and non-linear data points to low-dimensional embedding space, PC-SIsomap can effectively take advantage of pairwise constraint information to realize dimensionality reduction. At the same time in order to solve the out-of-sample problem in manifold learning, BP neural network is employed to build a nonlinear mapping relation from the high-dimensional original data space to a low-dimensional feature space. Consequentially, support vector machine (SVM) classifiers are designed for realizing pattern classification in the low-dimensional feature space. Some experiments are executed in UCI datasets and dataset of gas safety monitoring system in coal mine, the results show that PC-SIsomap not only reduces the residual value, but also improves the classification accuracy.