researcher

Swagatam Das

Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

8 papers hereh-index 7627k citations419 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position

middle author2
last author5

Across the 7 of 8 papers where every author was matched, so the position is known.

fields

affiliations

Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

Homepage

same name

Swagatam Das — 10 papers, h 2
Swagatam Das — 8 papers, h 5
Swagatam Das — 7 papers, h 1
Swagatam Das — 6 papers, h 17
Swagatam Das — 5 papers, h 4
Swagatam Das — 3 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity

20162023

most citedRecent Trends in the Use of Statistical Tests for Comparing Swarm and Evolutionary Computing Algorithms: Practical Guidelines and a Critical Review

508 citations · 585 across the 4 of their papers we have counts for

collaborators

Showing stat.MLShow all

2 papers · 1 filter

stat.ML2020★ 9 cited

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

Debolina Paul, Saptarshi Chakraborty, Swagatam Das +1

Kernel $k$ -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…

stat.ML2019★ 2 cited

A Strongly Consistent Sparse $k$ -means Clustering with Direct $l_{1}$ Penalization on Variable Weights

Saptarshi Chakraborty, Swagatam Das

We propose the Lasso Weighted $k$ -means ( $L W$ - $k$ -means) algorithm as a simple yet efficient sparse clustering procedure for high-dimensional data where the number of features ( $p$ …

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

A Strongly Consistent Sparse k-means Clustering with Direct l1​ Penalization on Variable Weights

A Strongly Consistent Sparse $k$ -means Clustering with Direct $l_{1}$ Penalization on Variable Weights