Supervised learning of sparse context reconstruction coefficients for data representation and classification
| Autor: | Jingbin Wang, Ming Yin, Benjamin Edwards, Xuejie Liu, Peijuan Xu |
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| Rok vydání: | 2015 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer science business.industry Iterative method Computer Vision and Pattern Recognition (cs.CV) Supervised learning Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Machine learning computer.software_genre External Data Representation Machine Learning (cs.LG) Computer Science - Learning 020901 industrial engineering & automation Data point Discriminative model Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) computer Software |
| Zdroj: | Neural Computing and Applications. 28:135-143 |
| ISSN: | 1433-3058 0941-0643 |
| Popis: | Context of data points, which is usually defined as the other data points in a data set, has been found to play important roles in data representation and classification. In this paper, we study the problem of using context of a data point for its classification problem. Our work is inspired by the observation that actually only very few data points are critical in the context of a data point for its representation and classification. We propose to represent a data point as the sparse linear combination of its context, and learn the sparse context in a supervised way to increase its discriminative ability. To this end, we proposed a novel formulation for context learning, by modeling the learning of context parameter and classifier in a unified objective, and optimizing it with an alternative strategy in an iterative algorithm. Experiments on three benchmark data set show its advantage over state-of-the-art context-based data representation and classification methods. Comment: arXiv admin note: substantial text overlap with arXiv:1507.00019 |
| Databáze: | OpenAIRE |
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