论文标题
空间变压器K-均值
Spatial Transformer K-Means
论文作者
论文摘要
K均值定义了最受使用的基于质心的聚类算法之一，其性能与数据的嵌入相关。复杂的数据嵌入旨在以$ k $ -MEANS的表演为代价，而其理论保证和结果的可解释性为代价。取而代之的是，我们建议保留具有与非刚性转换不变的相似性度量的固有数据空间和增强K-均值。这使得（i）减少与数据相关的内在麻烦，从而降低了聚类任务的复杂性并提高性能并产生最新的结果，（ii）在数据的输入空间中聚类，从而导致完全可解释的聚类聚类算法，以及（iii）convergence保证的收益。
K-means defines one of the most employed centroid-based clustering algorithms with performances tied to the data's embedding. Intricate data embeddings have been designed to push $K$-means performances at the cost of reduced theoretical guarantees and interpretability of the results. Instead, we propose preserving the intrinsic data space and augment K-means with a similarity measure invariant to non-rigid transformations. This enables (i) the reduction of intrinsic nuisances associated with the data, reducing the complexity of the clustering task and increasing performances and producing state-of-the-art results, (ii) clustering in the input space of the data, leading to a fully interpretable clustering algorithm, and (iii) the benefit of convergence guarantees.
