کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
532720 869986 2009 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Incremental subspace learning via non-negative matrix factorization
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Incremental subspace learning via non-negative matrix factorization
چکیده انگلیسی

In this paper we introduce an incremental non-negative matrix factorization (INMF) scheme in order to overcome the difficulties that conventional NMF has in online processing of large data sets. The proposed scheme enables incrementally updating its factors by reflecting the influence of each observation on the factorization appropriately. This is achieved via a weighted cost function which also allows controlling the memorylessness of the factorization. Unlike conventional NMF, with its incremental nature and weighted cost function the INMF scheme successfully utilizes adaptability to dynamic data content changes with a lower computational complexity. Test results reported for two video applications, namely background modeling in video surveillance and clustering, demonstrate that INMF is capable of online representing data content while reducing dimension significantly.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 42, Issue 5, May 2009, Pages 788–797
نویسندگان
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