深子空间聚类中的稀疏度测度

Samiran Das, Chirag Kyal, S. Pratiher
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引用次数: 0

摘要

传统的聚类方法根据相似度、连续性、邻域信息等属性对数据点进行分组,忽略了数据的结构属性。因此,普遍的集群方法在实际应用程序中的性能低于标准。与传统的聚类方法不同,子空间聚类方法试图对数据点进行分组,同时考虑到数据的固有结构和等级相关属性。尽管基于深度学习的方法发展迅速,但很少有研究将深度学习用于子空间聚类任务。这项工作引入了一种基于自编码器的深度学习架构,该架构由一个用于深度子空间聚类任务的自表达层组成。为了简化优化任务,我们使用光滑的L2, L0.5和Frobenius规范代替实际度量。我们还探讨了描述自我表达层的自我表示系数矩阵的稀疏度度量的有效性。在标准数据集上进行的实验表明,有效的稀疏度度量的应用提高了子空间聚类方法的性能,与之前的深子空间聚类方法相比,取得了更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Sparsity Measures In Deep Subspace Clustering
Traditional clustering methods groups data points according to attributes such as similarity, continuity, neighbor-hood information, etc. overlooks the structural properties of the data. Consequently, prevalent clustering approaches to below-par performance in real-world applications. Unlike traditional clustering approaches, subspace clustering methods attempt to group datapoints keeping the inherent structure and rank-related properties of the data into account. Despite the rapid growth in deep learning-based approaches, very few works have utilized deep learning for the subspace clustering task. This work introduced an auto-encoder-based deep learning architecture consisting of a self-expressive layer for the deep subspace clustering task. We use smoothed L2, L0.5 and Frobenius norms instead of the actual measures for ease of optimization task. We also explored the efficacy of sparsity measures that characterize the self-representation coefficient matrix of the self-expressive layer. The experiments conducted on standard datasets suggest that the application of efficient sparsity measures improves the performance of the subspace clustering approach and results in superior performance compared to the previous deep subspace clustering approaches.
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