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Found 8 papers, 3 papers with code
Robust and Automatic Data Clustering: Dirichlet Process meets Median-of-Means
Clustering stands as one of the most prominent challenges within the realm of unsupervised machine learning.
Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification
The problem of linear predictions has been extensively studied for the past century under pretty generalized frameworks.
Uniform Concentration Bounds toward a Unified Framework for Robust Clustering
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.
Robust Principal Component Analysis: A Median of Means Approach
Principal Component Analysis (PCA) is a fundamental tool for data visualization, denoising, and dimensionality reduction.
Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift Algorithm
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.
Kernel k-Means, By All Means: Algorithms and Strong Consistency
We show the method implicitly performs annealing in kernel feature space while retaining efficient, closed-form updates, and we rigorously characterize its convergence properties both from computational and statistical points of view.
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering
In a rich variety of real data clustering applications, PEA is shown to do as well as k-means for simple datasets, while dramatically improving performance in more complex settings.
Entropy Regularized Power k-Means Clustering
Despite its well-known shortcomings, $k$-means remains one of the most widely used approaches to data clustering.
