کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
405450 677636 2014 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Stable locality sensitive discriminant analysis for image recognition
ترجمه فارسی عنوان
تجزیه و تحلیل تجزیه و تحلیل حساس محل سکونت برای تشخیص تصویر
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Locality Sensitive Discriminant Analysis (LSDA) is one of the prevalent discriminant approaches based on manifold learning for dimensionality reduction. However, LSDA ignores the intra-class variation that characterizes the diversity of data, resulting in unstableness of the intra-class geometrical structure representation and not good enough performance of the algorithm. In this paper, a novel approach is proposed, namely stable locality sensitive discriminant analysis (SLSDA), for dimensionality reduction. SLSDA constructs an adjacency graph to model the diversity of data and then integrates it in the objective function of LSDA. Experimental results in five databases show the effectiveness of the proposed approach.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 54, June 2014, Pages 49–56
نویسندگان
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