منابع مشابه
Kernel discriminant analysis based feature selection
For two-class problems we propose two feature selection criteria based on kernel discriminant analysis (KDA). The first one is the objective function of kernel discriminant analysis called the KDA criterion. We show that the KDA criterion is monotonic for the deletion of features, which ensures stable feature selection. The second one is the recognition rate obtained by a KDA classifier, called...
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Increasing the use of Internet and some phenomena such as sensor networks has led to an unnecessary increasing the volume of information. Though it has many benefits, it causes problems such as storage space requirements and better processors, as well as data refinement to remove unnecessary data. Data reduction methods provide ways to select useful data from a large amount of duplicate, incomp...
متن کاملKernel Feature Selection
We address the problem of using a kernel spectral criterion function for feature selection. A feature selection paradigm using spectral properties of the affinity matrix of the input data was recently introduced in [11] and which leads to a bilinear interaction between entries of the data sample. Our goal in this paper is to extend the idea of spectral criteria for feature selection to higher o...
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Article history: Received 11 June 2014 Received in revised form 23 October 2014 Accepted 11 June 2015 Available online 19 June 2015
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We propose a novel kernel approach to dimension reduction for supervised learning: feature extraction and variable selection; the former constructs a small number of features from predictors, and the latter finds a subset of predictors. First, a method of linear feature extraction is proposed using the gradient of regression function, based on the recent development of the kernel method. In com...
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ژورنال
عنوان ژورنال: Bulletin of Electrical Engineering and Informatics
سال: 2020
ISSN: 2302-9285,2089-3191
DOI: 10.11591/eei.v9i4.1959