کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
532951 870027 2006 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Improving nearest neighbor classification with cam weighted distance
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Improving nearest neighbor classification with cam weighted distance
چکیده انگلیسی

Nearest neighbor (NN) classification assumes locally constant class conditional probabilities, and suffers from bias in high dimensions with a small sample set. In this paper, we propose a novel cam weighted distance to ameliorate the curse of dimensionality. Different from the existing neighborhood-based methods which only analyze a small space emanating from the query sample, the proposed nearest neighbor classification using the cam weighted distance (CamNN) optimizes the distance measure based on the analysis of inter-prototype relationship. Our motivation comes from the observation that the prototypes are not isolated. Prototypes with different surroundings should have different effects in the classification. The proposed cam weighted distance is orientation and scale adaptive to take advantage of the relevant information of inter-prototype relationship, so that a better classification performance can be achieved. Experiments show that CamNN significantly outperforms one nearest neighbor classification (1-NN) and kk-nearest neighbor classification (kk-NN) in most benchmarks, while its computational complexity is comparable with that of 1-NN classification.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 39, Issue 4, April 2006, Pages 635–645
نویسندگان
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