Khatri-Rao聚类用于数据摘要
Khatri-Rao Clustering for Data Summarization
AI总结：
Khatri-Rao聚类方法通过改进质心聚类，实现更简洁准确的数据摘要。
摘要：
随着数据集在规模和复杂性上持续增长，寻找简洁且准确的数据摘要已成为一个重要挑战。基于质心的聚类，一种广泛采用的方法，通过少数原型来寻找数据集的信息摘要，每个原型代表数据中的一个聚类。尽管它们被广泛应用，但由此产生的数据摘要往往包含冗余，限制了其在具有大量潜在聚类的数据集中的有效性。为克服这一限制，我们引入了Khatri-Rao聚类范式，将其扩展到传统基于质心的聚类，以产生更简洁但同样准确的数据摘要，假定质心源于两个或多个简洁的原型集的相互作用。我们研究了两种基于质心的聚类方法，即广为人知的k-means算法和日益流行的深度聚类主题，通过Khatri-Rao范式进行研究。为此，我们引入了Khatri-Rao k-means算法和Khatri-Rao深度聚类框架。广泛的实验表明，Khatri-Rao k-means在数据摘要的简洁性和准确性之间取得了更有利的权衡，优于标准k-means。利用表示学习，Khatri-Rao深度聚类框架提供了更大的益处，通过深度聚类减少数据摘要的大小，同时保持其准确性。
英文摘要：
As datasets continue to grow in size and complexity, finding succinct yet accurate data summaries poses a key challenge. Centroid-based clustering, a widely adopted approach to address this challenge, finds informative summaries of datasets in terms of few prototypes, each representing a cluster in the data. Despite their wide adoption, the resulting data summaries often contain redundancies, limiting their effectiveness particularly in datasets characterized by a large number of underlying clusters. To overcome this limitation, we introduce the Khatri-Rao clustering paradigm that extends traditional centroid-based clustering to produce more succinct but equally accurate data summaries by postulating that centroids arise from the interaction of two or more succinct sets of protocentroids. We study two central approaches to centroid-based clustering, namely the well-established k-Means algorithm and the increasingly popular topic of deep clustering, under the lens of the Khatri-Rao paradigm. To this end, we introduce the Khatri-Rao k-Means algorithm and the Khatri-Rao deep clustering framework. Extensive experiments show that Khatri-Rao k-Means can strike a more favorable trade-off between succinctness and accuracy in data summarization than standard k-Means. Leveraging representation learning, the Khatri-Rao deep clustering framework offers even greater benefits, reducing even more the size of data summaries given by deep clustering while preserving their accuracy.
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