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231224s2013 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2013.104
|2 doi
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|a eng
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| 100 |
1 |
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|a Chu, Delin
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Sparse canonical correlation analysis
|b new formulation and algorithm
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| 264 |
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|c 2013
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 07.03.2016
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| 500 |
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|a Date Revised 18.10.2013
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a In this paper, we study canonical correlation analysis (CCA), which is a powerful tool in multivariate data analysis for finding the correlation between two sets of multidimensional variables. The main contributions of the paper are: 1) to reveal the equivalent relationship between a recursive formula and a trace formula for the multiple CCA problem, 2) to obtain the explicit characterization for all solutions of the multiple CCA problem even when the corresponding covariance matrices are singular, 3) to develop a new sparse CCA algorithm, and 4) to establish the equivalent relationship between the uncorrelated linear discriminant analysis and the CCA problem. We test several simulated and real-world datasets in gene classification and cross-language document retrieval to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed method is competitive with the state-of-the-art sparse CCA algorithms
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| 650 |
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Liao, Li-Zhi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ng, Michael K
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Xiaowei
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 35(2013), 12 vom: 17. Dez., Seite 3050-65
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:35
|g year:2013
|g number:12
|g day:17
|g month:12
|g pages:3050-65
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|u http://dx.doi.org/10.1109/TPAMI.2013.104
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|d 35
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|h 3050-65
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