Presenter: Yanis Bahroun
Title: Spatial Transformer K-means
Abstract: 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 Kmeans performances at the cost of reduced theoretical guarantees and interpretability of the results. Instead, we propose preserving the intrinsic data space and augmenting 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, and (ii) clustering in the input space of the data, leading to a fully interpretable clustering algorithm, and (iii) the benefit of convergence guarantees.
Link to the arXiv: https://arxiv.org/pdf/2202.07829.pdf