Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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This paper proposes a semi-supervised distance metric learning algorithm for the ranking problem. Instead of giving the computer what are the important factors that affect the final rank value, we only give several most certainly ranked points which implicitly contain the knowledge of the ranking factors. Then the computer can automatically use the most certain points and plenty of unlabeded data to learn an informative metric for ranking. This metric not only can help to regress an order in the observed data, but also can be used to retrieve the data by querying new test points. Moreover, the lower-rank distance metric can be used to visualize high-dimensional data. We also present an application to the housing potential estimation problem. It is shown that the algorithm is efficient to help consultants to refine their consulting work.