Attributed Subspace Clustering
Attributed Subspace Clustering
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DOI：
10.24963/ijcai.2019/516
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发表时间：
2019
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影响因子：
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通讯作者：
Jing Wang;Linchuan Xu;Feng Tian;Atsushi Suzuki;Changqing Zhang;K. Yamanishi
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作者：
Jing Wang;Linchuan Xu;Feng Tian;Atsushi Suzuki;Changqing Zhang;K. Yamanishi
现有的基于表示的子空间聚类方法主要是将数据的所有特征作为一个整体来学习单一的自表示，从而得到一个聚类解。然而，真实的数据通常是复杂的，并且由多个属性或子特征组成，例如面部图像具有表情或性别。每个属性在描述数据时都是不同的和互补的。未能探索属性并捕获它们之间的互补信息可能导致不准确的表示。此外，单个聚类解决方案在描述数据方面相当有限，这些数据通常可以从不同的方面进行解释，并根据属性分为多个聚类。因此，我们提出了一个创新的模型称为属性子空间聚类（ASC）。它同时学习来自原始数据的潜在表示的多个自我表示。通过利用希尔伯特施密特独立准则作为共正则化项，ASC强制每个自我表示是独立的，并对应于一个特定的属性。一个更全面的自我表征，然后通过添加这些自我表征建立。几个基准图像数据集上的实验已经证明了ASC的有效性，不仅在集成表示所实现的聚类精度方面，而且在数据的多样性解释方面，这超出了当前方法所能提供的。
Existing methods on representation-based subspace clustering mainly treat all features of data as a whole to learn a single self-representation and get one clustering solution. Real data however are often complex and consist of multiple attributes or sub-features, such as a face image has expressions or genders. Each attribute is distinct and complementary on depicting the data. Failing to explore attributes and capture the complementary information among them may lead to an inaccurate representation. Moreover, a single clustering solution is rather limited to depict data, which can often be interpreted from different aspects and grouped into multiple clusters according to attributes. Therefore, we propose an innovative model called attributed subspace clustering (ASC). It simultaneously learns multiple self-representations on latent representations derived from original data. By utilizing Hilbert Schmidt Independence Criterion as a co-regularizing term, ASC enforces that each self-representation is independent and corresponds to a specific attribute. A more comprehensive self-representation is then established by adding these self-representations. Experiments on several benchmark image datasets have demonstrated the effectiveness of ASC not only in terms of clustering accuracy achieved by the integrated representation, but also the diverse interpretation of data, which is beyond what current approaches can offer.