원문정보
초록
영어
Under today’s big data environment, with the rapid development of computer network technology and information technology, data mining is becoming more and more important in computer science. Classification is one of the most important aspects in data mining research Field. Recently, representation methods, such as sparse representation and low rank representation, have been much concerned. They both have wide applications in scientific and engineering fields. However, sparse representation and low rank representation include many methods, although these methods have their own characteristics, they are all effective for handling classification problems. This paper focuses on the performance comparison of different representation methods currently used in handling classification problems and views other conventional methods that can be applied in this field.
목차
1. Introduction
2. Sparse Representation Methods for Classification
2.1. Multi-class Classification
2.2. Orthogonal Matching Pursuit
2.3. Sparse Representation-based Classification
2.4. Structured Sparse Representation
3. Collaborative Representation Methods for Classification
3.1. Collaborative Representation based on Methods for Classification
3.2. Collaborative Representation Optimized Classifier
4. Conventional Methods used for Classification
4.1. Nearest Neighbors
4.2. Nearest Subspace Classifier
5. Low Rank Representation Methods
6. Experiments
6.1. Face Recognition
6.2. Digit Recognition
7. The Comparison of Different Methods
8. Conclusions
Acknowledgements
References