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Robust and sparse canonical correlation analysis based L(2,p)-norm

التفاصيل البيبلوغرافية
العنوان: Robust and sparse canonical correlation analysis based L(2,p)-norm
المؤلفون: Zhong-rong Shi, Sheng Wang, Chuan-cai Liu
المصدر: The Journal of Engineering (2017)
بيانات النشر: Wiley, 2017.
سنة النشر: 2017
المجموعة: LCC:Engineering (General). Civil engineering (General)
مصطلحات موضوعية: feature selection, feature extraction, paired data, distance measurement, robust and sparse CCA, feature fusion method, group sparse feature selection, robust feature extraction, L(2)-norm distance minimization, canonical correlation analysis, objective function, RSCCA-based L(2,p)-norm, Engineering (General). Civil engineering (General), TA1-2040
الوصف: The objective function of canonical correlation analysis (CCA) is equivalent to minimising an L(2)-norm distance of the paired data. Owing to the characteristic of L(2)-norm, CCA is highly sensitive to noise and irrelevant features. To alleviate such problem, this study incorporates robust feature extraction and group sparse feature selection into the framework of CCA, and proposes a feature fusion method named robust and sparse CCA (RSCCA). In RSCCA, L(2,p)-norm is adopted as the distance measurement of paired data, which can alleviate the effect of noise and irrelevant features and achieve robust performance. The experimental results show that our method outperforms CCA and its variants for feature fusion.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2051-3305
61539090
العلاقة: http://digital-library.theiet.org/content/journals/10.1049/joe.2016.0296Test; https://doaj.org/toc/2051-3305Test
DOI: 10.1049/joe.2016.0296
الوصول الحر: https://doaj.org/article/d6153909076740c6beb08be5cb2acbfbTest
رقم الانضمام: edsdoj.6153909076740c6beb08be5cb2acbfb
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20513305
61539090
DOI:10.1049/joe.2016.0296