.png){kind=icon}
返回
Compressive-Projection Principal Component Analysis
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2009.2025089.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Principal component analysis (PCA) is often central to dimensionality reduction and compression in many applications, yet its data-dependent nature as a transform computed via expensive eigendecomposition often hinders its use in severely resource-constrained settings such as satellite-borne sensors. A process is presented that effectively shifts the computational burden of PCA from the resource-constrained encoder to a presumably more capable base-station decoder. The proposed approach, compressive-projection PCA (CPPCA), is driven by projections at the sensor onto lower-dimensional subspaces chosen at random, while the CPPCA decoder, given only these random projections, recovers not only the coefficients associated with the PCA transform, but also an approximation to the PCA transform basis itself. An analysis is presented that extends existing Rayleigh-Ritz theory to the special case of highly eccentric distributions; this analysis in turn motivates a reconstruction process at the CPPCA decoder that consists of a novel eigenvector reconstruction based on a convex-set optimization driven by Ritz vectors within the projected subspaces. As such, CPPCA constitutes a fundamental departure from traditional PCA in that it permits its excellent dimensionality-reduction and compression performance to be realized in an light-encoder/heavy-decoder system architecture. In experimental results, CPPCA outperforms a multiple-vector variant of compressed sensing for the reconstruction of hyperspectral data.
Keyword:
Hyperspectral data
principal component analysis (PCA)
random projections
Rayleigh-Ritz theory
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
机构
暂无机构信息
引用论文
分享收藏Configurational assignment of brassinosteroid sidechain by exciton coupled circular dichroic spectroscopy.png)
Tetrahedron
OAAI
TetrahedronIF0
OAAI分享收藏Algorithms for simultaneous sparse approximation. Part II: Convex relaxation同时稀疏逼近的算法。第二部分: 凸松弛SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏分享收藏Mapping of the male sterile mutant gene ftms in Brassica rapa L. ssp. pekinensis via BSR-Seq combined with whole-genome resequencing甘草型油菜雄性不育突变基因ftms的定位。通过bsr-seq结合全基因组重测序的pekinensis
PREAI
分享收藏分享收藏Short telomeres are associated with genetic complexity, high-risk genomic aberrations, and short survival in chronic lymphocytic leukemiaBloodOAAI
BloodIF0
OAAI分享收藏
查看更多内容