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Adaptive Independent Component Analysis by Modified Kernel Density Estimation.

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Název: Adaptive Independent Component Analysis by Modified Kernel Density Estimation.
Autoři: Xue, Yunfeng, Wang, Yujia, Han, Yujie
Zdroj: Advanced Intelligent Computing Theories & Applications; 2010, p230-237, 8p
Abstrakt: In this paper, an adaptive algorithm for linear instantaneous independent component analysis is proposed, which is is based on minimizing the mutual information contrast function. Adaptive density estimation by modified kernel density estimation is applied to estimate the unknown probability density functions as well as their first and second derivatives. Empirical comparisons with several popular algorithms confirm the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]
Copyright of Advanced Intelligent Computing Theories & Applications is the property of Springer Nature / Books and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Adaptive Independent Component Analysis by Modified Kernel Density Estimation.
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  Data: <searchLink fieldCode="AR" term="%22Xue%2C+Yunfeng%22">Xue, Yunfeng</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yujia%22">Wang, Yujia</searchLink><br /><searchLink fieldCode="AR" term="%22Han%2C+Yujie%22">Han, Yujie</searchLink>
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  Data: Advanced Intelligent Computing Theories & Applications; 2010, p230-237, 8p
– Name: Abstract
  Label: Abstract
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  Data: In this paper, an adaptive algorithm for linear instantaneous independent component analysis is proposed, which is is based on minimizing the mutual information contrast function. Adaptive density estimation by modified kernel density estimation is applied to estimate the unknown probability density functions as well as their first and second derivatives. Empirical comparisons with several popular algorithms confirm the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Advanced Intelligent Computing Theories & Applications is the property of Springer Nature / Books and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/978-3-642-14922-1_29
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      – Code: eng
        Text: English
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        StartPage: 230
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      – TitleFull: Adaptive Independent Component Analysis by Modified Kernel Density Estimation.
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            NameFull: Xue, Yunfeng
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            NameFull: Wang, Yujia
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            NameFull: Han, Yujie
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              Text: 2010
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              Y: 2010
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              Value: 9783642149214
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