Discriminative sparse embedding based on adaptive graph for dimension reduction
| Autor: | Kaibing Zhang, Weihua Ou, Zhonghua Liu, Kaiming Shi, Lin Wang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Iterative method Dimensionality reduction Nonlinear dimensionality reduction Pattern recognition 02 engineering and technology Matrix (mathematics) 020901 industrial engineering & automation Discriminative model Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) Embedding 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business Subspace topology |
| Zdroj: | Engineering Applications of Artificial Intelligence. 94:103758 |
| ISSN: | 0952-1976 |
| DOI: | 10.1016/j.engappai.2020.103758 |
| Popis: | The traditional manifold learning methods usually utilize the original observed data to directly define the intrinsic structure among data. Because the original samples often contain a deal of redundant information or it is corrupted by noises, it leads to the unreliability of the obtained intrinsic structure. In addition, the intrinsic structure learning and subspace learning are completely separated. For solving above problems, this paper presents a novel dimension reduction method termed discriminative sparse embedding (DSE) based on adaptive graph. By projecting the original samples into a low-dimensional subspace, DSE learns a sparse weight matrix, which can reduce the effects of redundant information and noises of the original data, and uncover essential structural relationship among the data. In DSE, the robust subspace is learned from the original data. Meanwhile, the intrinsic local structure and the optimal subspace can be simultaneously learned, in which they are mutually improved, and the accurate structure can be captured, and the optimal subspace can be obtained. We propose an alternative and iterative method to solve the DSE model. In order to evaluate the performance of DSE, it is compared with some state-of-the-art feature extraction algorithms. Various experiments show that our DSE is effective and feasible. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect