کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5004201 1461194 2016 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Discriminative sparse subspace learning and its application to unsupervised feature selection
ترجمه فارسی عنوان
یادگیری خردهفرهنگی فریبنده و کاربرد آن در انتخاب ویژگی بدون نظارت
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی کنترل و سیستم های مهندسی
چکیده انگلیسی
In order to efficiently use the intrinsic data information, in this study a Discriminative Sparse Subspace Learning (DSSL) model has been investigated for unsupervised feature selection. First, the feature selection problem is formulated as a subspace learning problem. In order to efficiently learn the discriminative subspace, we investigate the discriminative information in the subspace learning process. Second, a two-step TDSSL algorithm and a joint modeling JDSSL algorithm are developed to incorporate the clusters׳ assignment as the discriminative information. Then, a convergence analysis of these two algorithms is provided. A kernelized discriminative sparse subspace learning (KDSSL) method is proposed to handle the nonlinear subspace learning problem. Finally, extensive experiments are conducted on real-world datasets to show the superiority of the proposed approaches over several state-of-the-art approaches.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISA Transactions - Volume 61, March 2016, Pages 104-118
نویسندگان
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