9 accepted papers
Convex clustering is a well-regarded clustering method, resembling the similar centroid-based approach of Lloyd’s k-means, without requiring a predefined cluster count. It starts with each data point as its centroid and iteratively merges them. Despite its advantages, this method can fail when deali
Clustering approaches that utilize convex loss functions have recently attracted growing interest in the formation of compact data clusters. Although classical methods like kmeans and its wide family of variants are still widely used, all of them require the number of clusters (k) to be supplied as
The field of unpaired image-to-image translation has undergone a significant transformation with the introduction of Generative Adversarial Networks (GANs), with CycleGAN and DiscoGAN as prominent variants. While these models show impressive empirical performance, their statistical properties are…
Variational Autoencoders (VAEs) have gained significant popularity among researchers as a powerful tool for understanding unknown distributions based on limited samples. This popularity stems partly from their impressive performance and partly from their ability to provide meaningful feature represe…
Clustering complex high-dimensional data is particularly challenging as the signal-to-noise ratio in such data is significantly lower than their classical counterparts. This is mainly because most of the features describing a data point have little to no information about the natural grouping of the…
Recent progress in center-based clustering algorithms combats poor local minima by implicit annealing through a family of generalized means. These methods are variations of Lloyd’s celebrated k-means algorithm, and are most appropriate for spherical clusters such as those arising from Gaussian data.…
Mean shift is a simple interactive procedure that gradually shifts data points towards the mode which denotes the highest density of data points in the region. Mean shift algorithms have been effectively used for data denoising, mode seeking, and finding the number of clusters in a dataset in an aut…
Recent advances in center-based clustering continue to improve upon the drawbacks of Lloyd's celebrated $k$-means algorithm over $60$ years after its introduction. Various methods seek to address poor local minima, sensitivity to outliers, and data that are not well-suited to Euclidean measures of f…
Despite its well-known shortcomings, k-means remains one of the most widely used approaches to data clustering. Current research continues to tackle its flaws while attempting to preserve its simplicity. Recently, the power k-means algorithm was proposed to avoid poor local minima by annealing throu…