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Self-Weighted Clustering With Adaptive Neighbors.
IEEE Transactions on Neural Networks and Learning Systems ( IF 10.4 ) Pub Date : 2020-01-29 , DOI: 10.1109/tnnls.2019.2944565
Feiping Nie , Danyang Wu , Rong Wang , Xuelong Li

Many modern clustering models can be divided into two separated steps, i.e., constructing a similarity graph (SG) upon samples and partitioning each sample into the corresponding cluster based on SG. Therefore, learning a reasonable SG has become a hot issue in the clustering field. Many previous works that focus on constructing better SG have been proposed. However, most of them follow an ideal assumption that the importance of different features is equal, which is not adapted in practical applications. To alleviate this problem, this article proposes a self-weighted clustering with adaptive neighbors (SWCAN) model that can assign weights for different features, learn an SG, and partition samples into clusters simultaneously. In experiments, we observe that the SWCAN can assign weights for different features reasonably and outperform than comparison clustering models on synthetic and practical data sets.

中文翻译:

具有自适应邻居的自加权聚类。

许多现代聚类模型可以分为两个单独的步骤,即在样本上构建相似图(SG),然后根据SG将每个样本划分为相应的聚类。因此,学习合理的SG已经成为集群领域的热点问题。已经提出了许多先前的致力于构建更好的SG的工作。然而,它们中的大多数遵循理想的假设,即不同功能的重要性是相等的,这在实际应用中是不适应的。为了缓解此问题,本文提出了一种具有自适应邻居的自加权聚类(SWCAN)模型,该模型可以为不同的功能分配权重,学习SG并将样本同时划分为多个聚类。在实验中
更新日期:2020-01-29
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