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Nonlinear Semi-Supervised Metric Learning Via Multiple Kernels and Local Topology
- Source :
- International Journal of Neural Systems. 28:1750040
- Publication Year :
- 2018
- Publisher :
- World Scientific Pub Co Pte Lt, 2018.
-
Abstract
- Changing the metric on the data may change the data distribution, hence a good distance metric can promote the performance of learning algorithm. In this paper, we address the semi-supervised distance metric learning (ML) problem to obtain the best nonlinear metric for the data. First, we describe the nonlinear metric by the multiple kernel representation. By this approach, we project the data into a high dimensional space, where the data can be well represented by linear ML. Then, we reformulate the linear ML by a minimization problem on the positive definite matrix group. Finally, we develop a two-step algorithm for solving this model and design an intrinsic steepest descent algorithm to learn the positive definite metric matrix. Experimental results validate that our proposed method is effective and outperforms several state-of-the-art ML methods.
- Subjects :
- Mathematical optimization
Computer Networks and Communications
02 engineering and technology
Semi-supervised learning
Chebyshev distance
Pattern Recognition, Automated
Intrinsic metric
Metric k-center
03 medical and health sciences
0302 clinical medicine
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Cluster Analysis
Humans
Computer Simulation
Mathematics
General Medicine
Equivalence of metrics
Nonlinear Dynamics
Metric (mathematics)
020201 artificial intelligence & image processing
Supervised Machine Learning
String metric
Algorithms
030217 neurology & neurosurgery
Fisher information metric
Subjects
Details
- ISSN :
- 17936462 and 01290657
- Volume :
- 28
- Database :
- OpenAIRE
- Journal :
- International Journal of Neural Systems
- Accession number :
- edsair.doi.dedup.....c484fa9dd441f1432a12041720ad6351