کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6863838 1439525 2018 29 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Block principal component analysis for tensor objects with frequency or time information
ترجمه فارسی عنوان
بلوک تجزیه و تحلیل مولفه اصلی برای اشیای تانسور با اطلاعات فرکانس یا زمان
کلمات کلیدی
تانسورها، استخراج ویژگی، تشخیص چهره بیش از حد، تشخیص صبحگاهی، ماتریس بلوک، ماتریس کوواریانس،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Feature extraction is a prerequisite in many machine learning and data mining applications. As the advancement of data acquisition techniques, nowadays tensor objects are accumulated with respect to frequency or time information in a great number of fields. For instance color or hyperspectral faces in multichannel information, and human gait motion in time information are obtained. In this paper, we propose and develop a block principal component analysis (BPCA) to extract features for this kind of tensor objects. Our idea is to unfold tensor objects according to their spatial information and frequency/time information, and represent them in block matrix form. The corresponding covariance matrix for frequency/time information can be captured and used. The block eigen-decomposition of such covariance matrix is employed to seek for projection solution as features. Both reconstruction and classification problems can be solved via these projected features. Extensive experiments have been conducted on various face or gait databases to demonstrate the superiority of BPCA compared with existing methods such as PCA, (2D)2PCA, MPCA, and UMPCA in terms of effectiveness. Moreover, the proposed BPCA is competitively efficient compared to these existing methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 302, 9 August 2018, Pages 12-22
نویسندگان
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