کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6941457 | 1450111 | 2018 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Ordinal preserving matrix factorization for unsupervised feature selection
ترجمه فارسی عنوان
تقسیم بندی ماتریس حفظ شده برای انتخاب ویژگی های بدون نظارت
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کلمات کلیدی
انتخاب ویژگی بدون نظارت، تقسیم ماتریس، نگهداری ساختار محلی محلی، انعطاف پذیری و انعطاف پذیری کم
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Feature selection aims to remove the irrelevant and redundant features to reduce the dimensionality of data and increase the efficiency of learning algorithms. Specifically, unsupervised feature selection without any label information has become a challenging and significant task in machine learning applications. In this paper, a novel algorithm called Ordinal Preserving Matrix Factorization (OPMF), which incorporates matrix factorization, ordinal locality structure preserving and inner-product regularization into a unified framework, is proposed for feature selection. The advantages of our algorithm are three-fold. First, the ordinal locality property of original data is preserved by introducing a triplet-based loss function to the selected features, which is of great importance for distance-based classification and clustering tasks. Second, an inner product regularization term is incorporated into the proposed framework, so that the selected features obtained by our OPMF can be sparse and low redundant. Third, a simple and efficient iteratively updating algorithm is derived to solve the objective function of the proposed algorithm. Extensive experimental results on six datasets demonstrate that the proposed OPMF can obtain competitive performance compared to the existing state-of-the-art unsupervised feature selection approaches.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing: Image Communication - Volume 67, September 2018, Pages 118-131
Journal: Signal Processing: Image Communication - Volume 67, September 2018, Pages 118-131
نویسندگان
Yugen Yi, Wei Zhou, Qinghua Liu, Guoliang Luo, Jianzhong Wang, Yuming Fang, Caixia Zheng,