منابع مشابه
Sparse semiparametric discriminant analysis
In recent years, a considerable amount of work has been devoted to generalizing linear discriminant analysis to overcome its incompetence for high-dimensional classification (Witten and Tibshirani, 2011, Cai and Liu, 2011, Mai et al., 2012 and Fan et al., 2012). In this paper, we develop high-dimensional sparse semiparametric discriminant analysis (SSDA) that generalizes the normal-theory discr...
متن کاملSparse Discriminant Analysis
Classi cation in high-dimensional feature spaces where interpretation and dimension reduction are of great importance is common in biological and medical applications. For these applications standard methods such as microarrays, 1D NMR, and spectroscopy have become everyday tools for measuring thousands of features in samples of interest. The samples are often costly and therefore many problems...
متن کاملSparse Uncorrelated Linear Discriminant Analysis
In this paper, we develop a novel approach for sparse uncorrelated linear discriminant analysis (ULDA). Our proposal is based on characterization of all solutions of the generalized ULDA. We incorporate sparsity into the ULDA transformation by seeking the solution with minimum `1-norm from all minimum dimension solutions of the generalized ULDA. The problem is then formulated as a `1-minimizati...
متن کاملNon-Sparse Multiple Kernel Fisher Discriminant Analysis
Sparsity-inducing multiple kernel Fisher discriminant analysis (MK-FDA) has been studied in the literature. Building on recent advances in non-sparse multiple kernel learning (MKL), we propose a non-sparse version of MK-FDA, which imposes a general lp norm regularisation on the kernel weights. We formulate the associated optimisation problem as a semi-infinite program (SIP), and adapt an iterat...
متن کاملSparse multinomial kernel discriminant analysis (sMKDA)
Dimensionality reduction via canonical variate analysis (CVA) is important for pattern recognition and has been extended variously to permit more flexibility, e.g. by “kernelizing” the formulation. This can lead to over-fitting, usually ameliorated by regularization. Here, a method for sparse, multinomial kernel discriminant analysis (sMKDA) is proposed, using a sparse basis to control complexi...
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ژورنال
عنوان ژورنال: Journal of Computer Applications
سال: 2013
ISSN: 1001-9081
DOI: 10.3724/sp.j.1087.2012.01017