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عنوان فارسی مقاله: خوشه بندی چند نمایی خود گام از طریق یک تنظیم کننده وزنی نرم جدید
عنوان انگلیسی مقاله: Self-Paced Multi-View Clustering via a Novel Soft Weighted Regularizer
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم و محاسبات
کلمات کلیدی فارسی: خوشه بندی چند نمایی، یادگیری خود گام، سنجش وزنی نرم
کلمات کلیدی انگلیسی: Multi-view clustering, self-paced learning, soft weighting
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2954559
دانشگاه: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 8
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14047
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Related Work


III. Proposed Approach


IV. Experimental Results


V. Conclusion


Authors


Figures


References

نمونه متن انگلیسی مقاله

Abstract


Multi-view clustering (MVC), which can exploit complementary information of different views to enhance the clustering performance, has attracted people’s increasing attentions in recent years. However, existing multi-view clustering methods typically solve a non-convex problem, therefore are easily stuck into bad local minima. In addition, noisy data and outliers affect the clustering process negatively. In this paper, we propose self-paced multi-view clustering via a novel soft weighted regularizer (SPMVC) to address these issues. Specifically, SPMVC progressively selects samples to train the MVC model from simplicity to complexity in a self-paced manner. A novel soft weighted regularizer is proposed to further reduce the negative impact of outliers and noisy data. Experimental results on real-world data sets demonstrate the effectiveness of the proposed method.


Introduction


The aim of clustering [1] is to divide a set of objects into different groups such that similar objects will be grouped into the same cluster, while dissimilar ones are placed into different clusters. Clustering has been widely used in different fields, including pattern recognition, social network analysis, astronomical data analysis, information retrieval, and bioinformatics, etc. In the past couple of decades, a large number of clustering models have been proposed, such as k-means [2], fuzzy clustering [3], density-based clustering [4], [5], distribution-based clustering [6], [7], mean shift clustering [8], [9], consensus clustering [10]–[12], clustering based on deep neural networks [13], [14] etc. However, these conventional algorithms can only deal with single view clustering problems. In real-world clustering tasks, data sets are often described by multiple views, each providing a specific aspect of data. To take full advantage of complementary information from different views, multi-view clustering was proposed [15]. Recently, a number of multi-view clustering methods [16]–[23] have been proposed and have been proved to be effective in solving multi-view clustering problems. However, existing multi-view clustering methods typically solve a non-convex optimization problem [24], which results in the consequence that they get trapped in bad local minima easily. To address the non-convexity issue, an effective and efficient way is to use curriculum learning [25] and self-paced learning [26]. The core idea of curriculum learning and self-paced learning is imitating the mechanisms of cognition of humans.

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