.png){kind=icon}
返回
Fast transformation-invariant component analysis
.png){kind=icon}
PRE
AI
DOI：10.1007/s11263-007-0094-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Dimensionality reduction techniques such as principal component analysis and factor analysis are used to discover a linear mapping between high-dimensional data samples and points in a lower-dimensional subspace. Previously, Frey and Jojic introduced transformation-invariant component analysis (TCA) to learn a linear mapping, invariant to a set of known form of global transformations. However, parameter estimation in that model using the previously-proposed expectation maximization (EM) algorithm required scalar operations in the order of N-2 where N is the dimensionality of each training example. This is prohibitive for many applications of interest such as modeling mid-to large-size images, where, for instance, N may be as high as 786432 (512 x 512 RGB image). In this paper, we present an efficient algorithm that reduces the computational requirements to order of N log N. With this speedup, we show the effectiveness of transformation-invariant component analysis in various applications including tracking, learning video textures, clustering, object recognition and object detection in images. Software for TCA can be downloaded from http://www.psi.toronto.edu/ fastTCA.htm.
Keyword:
TCA
patch
EM algorithm
dimensionality reduction
clustering
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:
9.3
论文数:
3.9K
被引数:
2.8W
机构
引用论文
.png){kind=icon}
Experimental Investigation to Monitor Tailpipe Emissions Entering into Vehicle Cabin to Improve Indoor Air Quality (IAQ)监测进入车厢的尾气排放以改善室内空气质量 (IAQ) 的实验研究
PREAI
分享收藏EigenTracking: Robust matching and tracking of articulated objects using a view-based representation
PREAI
分享收藏
查看更多内容