کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
407443 678140 2016 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Locality sensitive batch feature extraction for high-dimensional data
ترجمه فارسی عنوان
قابلیت استخراج ویژگی های دسته ای حساس برای داده های با ابعاد بزرگ
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

For feature extraction, the dimensionality of the feature space is usually much larger than the size of training set. This is known as under sample problem. At this time, local structure is more important than global structure. In this paper, locality sensitive batch feature extraction (LSBFE) is derived based on a new gradient optimization model by exploiting both local and global discriminant structure of data manifold. With the proposed LSBFE, a set of features can be extracted simultaneously. Recognition rate is improved compared with batch feature extraction (BFE), which only considers global information. It is shown that the proposed method achieves good performance for face databases, handwritten digit database, object database and DBWorld data set.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 171, 1 January 2016, Pages 664–672
نویسندگان
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