کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4947625 1439589 2017 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multi-view metric learning based on KL-divergence for similarity measurement
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Multi-view metric learning based on KL-divergence for similarity measurement
چکیده انگلیسی
In the past decades, we have witnessed a surge of interests of learning distance metrics for various image processing tasks. However, facing with features from multiple views, most metric learning methods fail to integrate compatible and complementary information from multi-view features to train a common distance metric. Most information is thrown away by those single-view methods, which affects their performances severely. Therefore, how to fully exploit information from multiple views to construct an optimal distance metric is of vital importance but challenging. To address this issue, this paper constructs a multi-view metric learning method which utilizes KL-divergences to integrate features from multiple views. Minimizing KL-divergence between features from different views can lead to the consistency of multiple views, which enables MML to exploit information from multiple views. Various experiments on several benchmark multi-view datasets have verified the excellent performance of this novel method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 238, 17 May 2017, Pages 269-276
نویسندگان
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