| k− Means clustering with a new divergence-based distance metric: Convergence and performance analysis S Chakraborty, S Das Pattern Recognition Letters 100, 67-73, 2017 | 74 | 2017 |
| Entropy weighted power k-means clustering S Chakraborty, D Paul, S Das, J Xu International conference on artificial intelligence and statistics, 691-701, 2020 | 72 | 2020 |
| Detecting meaningful clusters from high-dimensional data: A strongly consistent sparse center-based clustering approach S Chakraborty, S Das IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (6), 2894-2908, 2020 | 41 | 2020 |
| Simultaneous variable weighting and determining the number of clustersˇXA weighted Gaussian means algorithm S Chakraborty, S Das Statistics & Probability Letters 137, 148-156, 2018 | 40 | 2018 |
| Hierarchical clustering with optimal transport S Chakraborty, D Paul, S Das Statistics & probability letters 163, 108781, 2020 | 37 | 2020 |
| Automated clustering of high-dimensional data with a feature weighted mean shift algorithm S Chakraborty, D Paul, S Das Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 6930-6938, 2021 | 18 | 2021 |
| Uniform concentration bounds toward a unified framework for robust clustering D Paul, S Chakraborty, S Das, J Xu Advances in Neural Information Processing Systems 34, 8307-8319, 2021 | 16 | 2021 |
| Implicit annealing in kernel spaces: A strongly consistent clustering approach D Paul, S Chakraborty, S Das, J Xu IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (5), 5862-5871, 2022 | 15* | 2022 |
| Robust principal component analysis: a median of means approach D Paul, S Chakraborty, S Das IEEE Transactions on Neural Networks and Learning Systems, 2023 | 13 | 2023 |
| On the strong consistency of feature‐weighted k‐means clustering in a nearmetric space S Chakraborty, S Das Stat 8 (1), e227, 2019 | 13 | 2019 |
| Biconvex clustering S Chakraborty, J Xu Journal of Computational and Graphical Statistics 32 (4), 1524-1536, 2023 | 9 | 2023 |
| On the uniform concentration bounds and large sample properties of clustering with Bregman divergences D Paul, S Chakraborty, S Das Stat 10 (1), e360, 2021 | 8 | 2021 |
| -Entropy: A New Measure of Uncertainty with Some Applications S Chakraborty, D Paul, S Das 2021 IEEE International Symposium on Information Theory (ISIT), 1475-1480, 2021 | 7 | 2021 |
| Bregman power k-means for clustering exponential family data A Vellal, S Chakraborty, JQ Xu International Conference on Machine Learning, 22103-22119, 2022 | 5 | 2022 |
| On consistent entropy-regularized k-means clustering with feature weight learning: Algorithm and statistical analyses S Chakraborty, D Paul, S Das IEEE Transactions on Cybernetics 53 (8), 4779-4790, 2022 | 5 | 2022 |
| On the statistical properties of generative adversarial models for low intrinsic data dimension S Chakraborty, PL Bartlett arXiv preprint arXiv:2401.15801, 2024 | 4 | 2024 |
| On uniform concentration bounds for bi-clustering by using the VapnikˇVChervonenkis theory S Chakraborty, S Das Statistics & Probability Letters 175, 109102, 2021 | 4 | 2021 |
| Principal ellipsoid analysis (PEA): Efficient non-linear dimension reduction & clustering D Paul, S Chakraborty, D Li, D Dunson arXiv preprint arXiv:2008.07110, 2020 | 4 | 2020 |
| Clustering High-dimensional Data with Ordered Weighted Regularization C Chakraborty, S Paul, S Chakraborty, S Das International Conference on Artificial Intelligence and Statistics, 7176-7189, 2023 | 1 | 2023 |
| t-Divergence: A New Divergence Measure with Application to Robust Statistics & Clustering D Paul, S Chakraborty, S Das The Twelfth International Conference on Learning Representations, 2024 | | 2024 |
Swagatam DasProfessor, Electronics and Communication Sciences Unit, Indian Statistical Institute, KolkataVerified email at isical.ac.in
