Search Results for author: Debolina Paul

Found 8 papers, 3 papers with code

Robust and Automatic Data Clustering: Dirichlet Process meets Median-of-Means

no code implementations26 Nov 2023 Supratik Basu, Jyotishka Ray Choudhury, Debolina Paul, Swagatam Das

Clustering stands as one of the most prominent challenges within the realm of unsupervised machine learning.

Clustering

Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification

no code implementations6 Jan 2022 Saptarshi Chakraborty, Debolina Paul, Swagatam Das

The problem of linear predictions has been extensively studied for the past century under pretty generalized frameworks.

valid

Uniform Concentration Bounds toward a Unified Framework for Robust Clustering

1 code implementation NeurIPS 2021 Debolina Paul, Saptarshi Chakraborty, Swagatam Das, Jason Xu

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.

Clustering

Robust Principal Component Analysis: A Median of Means Approach

no code implementations5 Feb 2021 Debolina Paul, Saptarshi Chakraborty, Swagatam Das

Principal Component Analysis (PCA) is a fundamental tool for data visualization, denoising, and dimensionality reduction.

Data Visualization Denoising +2

Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift Algorithm

1 code implementation20 Dec 2020 Saptarshi Chakraborty, Debolina Paul, Swagatam Das

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.

Clustering Denoising +1

Kernel k-Means, By All Means: Algorithms and Strong Consistency

no code implementations12 Nov 2020 Debolina Paul, Saptarshi Chakraborty, Swagatam Das, Jason Xu

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.

Clustering

Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering

no code implementations17 Aug 2020 Debolina Paul, Saptarshi Chakraborty, Didong Li, David Dunson

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.

Clustering Computational Efficiency +1

Entropy Regularized Power k-Means Clustering

1 code implementation10 Jan 2020 Saptarshi Chakraborty, Debolina Paul, Swagatam Das, Jason Xu

Despite its well-known shortcomings, $k$-means remains one of the most widely used approaches to data clustering.

Clustering

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