Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning
- 18 May 2017
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks and Learning Systems
- Vol. 29 (2162237X) , 1-12
Abstract
In this paper, we address the semisupervised distance metric learning problem and its applications in classification and image retrieval. First, we formulate a semisupervised distance metric learning model by considering the metric information of inner classes and interclasses. In this model, an adaptive parameter is designed to balance the inner metrics and intermetrics by using data structure. Second, we convert the model to a minimization problem whose variable is symmetric positive-definite matrix. Third, in implementation, we deduce an intrinsic steepest descent method, which assures that the metric matrix is strictly symmetric positive-definite at each iteration, with the manifold structure of the symmetric positive-definite matrix manifold. Finally, we test the proposed algorithm on conventional data sets, and compare it with other four representative methods. The numerical results validate that the proposed method significantly improves the classification with the same computational efficiency.Keywords
Funding Information
- National Natural Science Foundation of China (11471208, 61471231, 11131006, 91648205, 61273298)
- Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University China
This publication has 43 references indexed in Scilit:
- Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature LearningIEEE Transactions on Cybernetics, 2015
- A Distributed Approach Toward Discriminative Distance Metric LearningIEEE Transactions on Neural Networks and Learning Systems, 2014
- LogDet Divergence-Based Metric Learning With Triplet Constraints and Its ApplicationsIEEE Transactions on Image Processing, 2014
- Information-Theoretic Semi-Supervised Metric Learning via Entropy RegularizationNeural Computation, 2014
- An Explicit Nonlinear Mapping for Manifold LearningIEEE Transactions on Cybernetics, 2012
- Semi-supervised sparse metric learning using alternating linearization optimization2010
- Learning an Intrinsic-Variable Preserving Manifold for Dynamic Visual TrackingIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009
- Comments on "Efficient and Robust Feature Extraction by Maximum Margin CriterionIEEE Transactions on Neural Networks, 2007
- A Differential Geometric Approach to the Geometric Mean of Symmetric Positive-Definite MatricesSIAM Journal on Matrix Analysis and Applications, 2005
- Nonlinear Dimensionality Reduction by Locally Linear EmbeddingScience, 2000