9 citations · 13 across the 10 of their papers we have counts for
11 papers
Convex Clustering Redefined: Robust Learning with the Median of Means Estimator
Sourav De, Koustav Chowdhury, Bibhabasu Mandal +4
Clustering approaches that utilize convex loss functions have recently attracted growing interest in the formation of compact data clusters. Although classical methods like k-means…
A New Framework for Convex Clustering in Kernel Spaces: Finite Sample Bounds, Consistency and Performance Insights
Shubhayan Pan, Kushal Bose, Debolina Paul +2
Convex clustering is a well-regarded clustering method, resembling the similar centroid-based approach of Lloyd's -means, without requiring a predefined cluster count. It starts…
Dirichlet Process-based Robust Clustering using the Median-of-Means Estimator
Supratik Basu, Jyotishka Ray Choudhury, Debolina Paul +1
Clustering stands as one of the most prominent challenges in unsupervised machine learning. Among centroid-based methods, the classic -means algorithm, based on Lloyd's heuristi…
Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification
Saptarshi Chakraborty, Debolina Paul, Swagatam Das
The problem of linear predictions has been extensively studied for the past century under pretty generalized frameworks. Recent advances in the robust statistics literature allow u…
Uniform Concentration Bounds toward a Unified Framework for Robust Clustering
Debolina Paul, Saptarshi Chakraborty, Swagatam Das +1
Recent advances in center-based clustering continue to improve upon the drawbacks of Lloyd's celebrated -means algorithm over years after its introduction. Various methods…
t-Entropy: A New Measure of Uncertainty with Some Applications
Saptarshi Chakraborty, Debolina Paul, Swagatam Das
The concept of Entropy plays a key role in Information Theory, Statistics, and Machine Learning.This paper introduces a new entropy measure, called the t-entropy, which exploits th…
Robust Principal Component Analysis: A Median of Means Approach
Debolina Paul, Saptarshi Chakraborty, Swagatam Das
Principal Component Analysis (PCA) is a fundamental tool for data visualization, denoising, and dimensionality reduction. It is widely popular in Statistics, Machine Learning, Comp…
Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift Algorithm
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. Mean shift algorithm…
Kernel k-Means, By All Means: Algorithms and Strong Consistency
Debolina Paul, Saptarshi Chakraborty, Swagatam Das +1
Kernel -means clustering is a powerful tool for unsupervised learning of non-linearly separable data. Since the earliest attempts, researchers have noted that such algorithms of…
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering
Debolina Paul, Saptarshi Chakraborty, Didong Li +1
Even with the rise in popularity of over-parameterized models, simple dimensionality reduction and clustering methods, such as PCA and k-means, are still routinely used in an amazi…
Entropy Regularized Power k-Means Clustering
Saptarshi Chakraborty, Debolina Paul, Swagatam Das +1
Despite its well-known shortcomings, -means remains one of the most widely used approaches to data clustering. Current research continues to tackle its flaws while attempting to…