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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Self-Weighted Clustering With Adaptive Neighbors
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Self-Weighted Clustering With Adaptive Neighbors

机译：自加权聚类与自适应邻居

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摘要

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.
机译：许多现代聚类模型可以分为两个分离的步骤，即，在样本时构建相似性图（SG）并基于SG将每个样本划分为相应的集群。因此，学习合理的SG已成为聚类字段中的一个热门问题。已经提出了许多专注于构建更好SG的工作。然而，大多数人遵循理想的假设，即不同特征的重要性等于，这在实际应用中不适应。为了缓解这个问题，本文提出了一种自加权聚类，其具有自适应邻居（SWCAN）模型，可以为不同特征分配权重，同时将SG学习和分区样本分配给群集。在实验中，我们观察到，SWCAN可以合理地为不同特征分配权重，而不是合成和实际数据集上的比较聚类模型。

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