期刊
PATTERN RECOGNITION LETTERS
卷 33, 期 9, 页码 1163-1170出版社
ELSEVIER
DOI: 10.1016/j.patrec.2012.02.007
关键词
Dimensionality reduction; Sparse models; PCA; Supervised learning; Un-supervised learning; Compressive sensing
资金
- Charles Stint University [OPA 4818]
- Australian Government
- Australian Research Council through the ICT Centre of Excellence
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. (C) 2012 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据