کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
407152 678130 2016 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A regularized least square based discriminative projections for feature extraction
ترجمه فارسی عنوان
یک پیش بینی های تشریحی برای اندازه گیری ویژگی های حداقل مربع منظم
کلمات کلیدی
کمترین مربع منظم نمایندگی انحصاری، نمایندگی همکاری، استخراج ویژگی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

In this paper, we present a regularized least square based discriminative projections (RLSDP) method for feature extraction. First, we show that both sparse representation based classifier (SRC) and collaborative representation based classification (CRC) are regularized least square in nature. Second, a regularized least square based graph embedding framework (RLSGE) is constructed. Third, a RLSGE based feature extraction method is given, named regularized least square based discriminant projections (RLSDP). In RLSDP, the within-class compactness information is characterized by the reconstruction residual from the same class, which is consistent with the idea of reconstruction; the between-class separability information is characterized by the between-class scatter matrix like Fisher LDA. RLSDP is much faster than SPP since RLSDP adopts the L2 norm constraint while SPP adopts the L1 norm constraint. The experimental results on AR face database, FERET face database, and the PolyU FKP database demonstrate that RLSDP works well in feature extraction and has a great recognition performance.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 175, Part A, 29 January 2016, Pages 198–205
نویسندگان
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