کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6865257 1439555 2018 43 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Evolutionary nonnegative matrix factorization with adaptive control of cluster quality
ترجمه فارسی عنوان
فاکتور سازی ماتریس غیر انتگرال تکاملی با کنترل تطبیقی ​​کیفیت خوشه
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Nonnegative matrix factorization (NMF) approximates a given data matrix using linear combinations of a small number of nonnegative basis vectors, weighted by nonnegative encoding coefficients. This enables the exploration of the cluster structure of the data through the examination of the values of the encoding coefficients and therefore, NMF is often used as a popular tool for clustering analysis. However, its encoding coefficients do not always reveal a satisfactory cluster structure. To improve its effectiveness, a novel evolutionary strategy is proposed here to drive the iterative updating scheme of NMF and generate encoding coefficients of higher quality that are capable of offering more accurate and sharper cluster structures. The proposed hybridization procedure that relies on multiple initializations reinforces the robustness of the solution. Additionally, three evolving rules are designed to simultaneously boost the cluster quality and the reconstruction error during the iterative updates. Any clustering performance measure, such as either an internal one relying on the data itself or an external based on the availability of ground truth information, can be employed to drive the evolving procedure. The effectiveness of the proposed method is demonstrated via careful experimental designs and thorough comparative analyses using multiple benchmark datasets.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 272, 10 January 2018, Pages 237-249
نویسندگان
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