Simple and Scalable Sparse k-means Clustering via Feature Ranking
Simple and Scalable Sparse k-means Clustering via Feature Ranking
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发表时间：
2020-02
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通讯作者：
Zhiyue Zhang;K. Lange;Jason Xu
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作者：
Zhiyue Zhang;K. Lange;Jason Xu
聚类是无监督学习中的一项基本活动，当特征空间是高维时，聚类是非常困难的。幸运的是，在许多现实场景中，只有少数特征与区分聚类相关。这激发了稀疏聚类技术的发展，这种技术通常依赖于高计算复杂度的外部算法中的k均值。目前的技术还需要仔细调整收缩参数，进一步限制了它们的可扩展性。在本文中，我们提出了一个新的框架，稀疏k均值聚类，是直观的，简单的实现，并具有竞争力的国家的最先进的算法。我们表明，我们的算法享有一致性和收敛性的保证。我们的核心方法很容易推广到几个特定于任务的算法，如聚类属性的子集和部分观察到的数据设置。我们通过模拟实验和真实的数据基准，包括三体小鼠蛋白质表达的案例研究，彻底展示了这些贡献。
Clustering, a fundamental activity in unsupervised learning, is notoriously difficult when the feature space is high-dimensional. Fortunately, in many realistic scenarios, only a handful of features are relevant in distinguishing clusters. This has motivated the development of sparse clustering techniques that typically rely on k-means within outer algorithms of high computational complexity. Current techniques also require careful tuning of shrinkage parameters, further limiting their scalability. In this paper, we propose a novel framework for sparse k-means clustering that is intuitive, simple to implement, and competitive with state-of-the-art algorithms. We show that our algorithm enjoys consistency and convergence guarantees. Our core method readily generalizes to several task-specific algorithms such as clustering on subsets of attributes and in partially observed data settings. We showcase these contributions thoroughly via simulated experiments and real data benchmarks, including a case study on protein expression in trisomic mice.