Dimensionality reduction via compressive sensing

详细信息	查看全文 | 推荐本文 |

• 作者：Junbin Gao^a ; ^{jbgao@csu.edu.au} ; Qinfeng Shi^b ; ^{qinfeng.shi@ieee.org} ; Tibé ; rio S. Caetano^c ; ^{Tiberio.Caetano@nicta.com.au}
• 关键词：Dimensionality reduction ; Sparse models ; PCA ; Supervised learning ; Un-supervised learning ; Compressive sensing
• 刊名：Pattern Recognition Letters
• 出版年：2012
• 期刊代码：101_01678655
• 类别：cp
• 出版时间：1 July, 2012
• 卷：33
• 期：9
• 页码：1163-1170
• 文件大小：2227 K

摘要

Compressive sensing is an emerging field predicated upon the fact that, if a signal has a sparse representation in some basis, then it can be almost exactly reconstructed from very few random measurements. Many signals and natural images, for example under the wavelet basis, have very sparse representations, thus those signals and images can be recovered from a small amount of measurements with very high accuracy. This paper is concerned with the dimensionality reduction problem based on the compressive assumptions. We propose novel unsupervised and semi-supervised dimensionality reduction algorithms by exploiting sparse data representations. The experiments show that the proposed approaches outperform state-of-the-art dimensionality reduction methods.