کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4969537 | 1449976 | 2017 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel framework for parsimonious multivariate analysis
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
This paper proposes a framework in which a multivariate analysis method (MVA) guides a selection of input variables that leads to a sparse feature extraction. This framework, called parsimonious MVA, is specially suited for high dimensional data such as gene arrays, digital pictures, etc. The feature selection relies on the analysis of consistency in the behaviour of the input variables through the elements of an ensemble of MVA projection matrices. The ensemble is constructed following a bootstrap that builds on an efficient and generalized MVA formulation that covers PCA, CCA and OPLS. Moreover, it allows the estimation of the relative relevance of each selected input variable. Experimental results point out that the features extracted by the parsimonious MVA have excellent discrimination power, comparing favorably with state-of-the-art methods, and are potentially useful to build interpretable features. Besides, the parsimonious feature extractor is shown to be robust against to parameter selection, as we all computationally efficient.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 71, November 2017, Pages 173-186
Journal: Pattern Recognition - Volume 71, November 2017, Pages 173-186
نویسندگان
Sergio Muñoz-Romero, Vanessa Gómez-Verdejo, Emilio Parrado-Hernández,