Semi-supervised linear discriminant analysis
نویسندگان
چکیده
منابع مشابه
Heteroscedastic Probabilistic Linear Discriminant Analysis with Semi-supervised Extension
Linear discriminant analysis (LDA) is a commonly used method for dimensionality reduction. Despite its successes, it has limitations under some situations, including the small sample size problem, the homoscedasticity assumption that different classes have the same Gaussian distribution, and its inability to produce probabilistic output and handle missing data. In this paper, we propose a semi-...
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Heteroscedastic Linear Discriminant Analysis (HLDA) was introduced in [1] as an extension of Linear Discriminant Analysis to the case where the class-conditional distributions have unequal covariances. The HLDA transform is computed such that the likelihood of the training (labeled) data is maximized, under the constraint that the projected distributions are orthogonal to a nuisance space that ...
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Deming Zhai1 [email protected] Hong Chang2 [email protected] Bo Li1 [email protected] Shiguang Shan2 [email protected] Xilin Chen2 [email protected] Wen Gao13 [email protected] 1 School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China 2 Key Laboratory of Intelligent Information Processing, Chinese Academy of Sciences, Beijing,China 3 Institute of Digital Media, Peking Univ...
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ژورنال
عنوان ژورنال: Journal of Chemometrics
سال: 2011
ISSN: 0886-9383
DOI: 10.1002/cem.1408