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Search - Kernel Entropy Component Analysis - List
[
Graph program
]
KECA
DL : 4
Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码,文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010. We introduce kernel entropy component analysis (kernel ECA) as a new method for data transformation and dimensionality reduction. Kernel ECA reveals structure relating to the Renyi entropy of the input space data set, estimated via a kernel matrix using Parzen windowing. This is achieved by projections onto a subset of entropy preserving kernel principal component analysis (kernel PCA) axes. This subset does not need, in general, to correspond to the top eigenvalues of the kernel matrix, in contrast to the dimensionality reduction using kernel PCA. We show that kernel ECA may produce strikingly different transformed data sets compared to kernel PCA, with a distinct angle-based structure. A new spectral clustering algorithm utilizing this structure is developed with positive results. Furthermore, kernel ECA is shown to be an useful alternative for pattern denoising.
Date
: 2025-07-09
Size
: 3kb
User
:
johhnny
[
AI-NN-PR
]
kernelECA
DL : 0
该程序提供了核熵成分分析的matlab实现,可应用于机器学习-Kernel Entropy Component Analysis
Date
: 2025-07-09
Size
: 1kb
User
:
Alex
[
Other
]
Kernel-Entropy-Component-Analysis
DL : 0
这篇文章是一种特征降维的方法,不过跟一般的计较离散度的方法不同,这个特征变换的准则是:尽可能地保留原始样本的熵-Kernel Entropy Component Analysis Jenssen, R Pattern Analysis and Machine Intelligence, IEEE Transactions on Volume: PP , Issue: 99
Date
: 2025-07-09
Size
: 1.14mb
User
:
本泽马
[
Other
]
kernel_eca-master
DL : 0
Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码,文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010.(We introduce kernel entropy component analysis (kernel ECA) as a new method for data transformation and dimensionality reduction. Kernel ECA reveals structure relating to the Renyi entropy of the input space data set, estimated via a kernel matrix using Parzen windowing. This is achieved by projections onto a subset of entropy preserving kernel principal component analysis (kernel PCA) axes. This subset does not need, in general, to correspond to the top eigenvalues of the kernel matrix, in contrast to the dimensionality reduction using kernel PCA. We show that kernel ECA may produce strikingly different transformed data sets compared to kernel PCA, with a distinct angle-based structure. A new spectral clustering algorithm utilizing this structure is developed with positive results. Furthermore, kernel ECA is shown to be an useful alternative for pattern denoising.)
Date
: 2025-07-09
Size
: 8kb
User
:
daxingxing001
[
Other
]
KECA_Journal_Article
DL : 0
Robert Jenssen 撰写论文原文(We introduce kernel entropy component analysis (kernel ECA) as a new method for data transformation and dimensionality reduction. Kernel ECA reveals structure relating to the Renyi entropy of the input space data set, estimated via a kernel matrix using Parzen windowing. This is achieved by projections onto a subset of entropy preserving kernel principal component analysis (kernel PCA) axes)
Date
: 2025-07-09
Size
: 1.14mb
User
:
小刀418
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