9 citations · 13 across the 4 of their papers we have counts for
6 papers
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…
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…