activity
20162026
most citedOn perfect clustering of high dimension, low sample size data

56 citations · 90 across the 11 of their papers we have counts for

collaborators

11 papers

stat.ME2026

A scalable version of MADD for big-data classification

Annesha Ghosh, Adrija Saha, Soham Sarkar

Distance-based classifiers are very popular, and the Euclidean distance is one of the most commonly used metrics in distance-based classifiers. However, classifiers based on the Eu…

stat.ME2024★ 12 cited

Classification Using Global and Local Mahalanobis Distances

Annesha Ghosh, Anil K. Ghosh, Rita SahaRay +1

We propose a novel semiparametric classifier based on Mahalanobis distances of an observation from the competing classes. Our tool is a generalized additive model with the logistic…

stat.ME2022

Nearest Neighbor Classification based on Imbalanced Data: A Statistical Approach

Anvit Garg, Anil K. Ghosh, Soham Sarkar

When the competing classes in a classification problem are not of comparable size, many popular classifiers exhibit a bias towards larger classes, and the nearest neighbor classifi…

stat.ME2022★ 1 cited

On Exact Feature Screening in Ultrahigh-dimensional Binary Classification

Sarbojit Roy, Soham Sarkar, Subhajit Dutta +1

We propose a new model-free feature screening method based on energy distances for ultrahigh-dimensional binary classification problems. With a high probability, the proposed metho…

stat.ME2021★ 10 cited

CovNet: Covariance Networks for Functional Data on Multidimensional Domains

Soham Sarkar, Victor M. Panaretos

Covariance estimation is ubiquitous in functional data analysis. Yet, the case of functional observations over multidimensional domains introduces computational and statistical cha…

math.ST2020

Separable Expansions for Covariance Estimation

Tomas Masak, Soham Sarkar, Victor M. Panaretos

The non-parametric estimation of covariance lies at the heart of functional data analysis, whether for curve or surface-valued data. The case of a two-dimensional domain poses both…

stat.ME2020

On a Generalization of the Average Distance Classifier

Sarbojit Roy, Soham Sarkar, Subhajit Dutta

In high dimension, low sample size (HDLSS)settings, the simple average distance classifier based on the Euclidean distance performs poorly if differences between the locations get…

cond-mat.soft2019

Choline chloride as a nano-crowder protects HP-36 from urea-induced denaturation: Insights from Solvent Dynamics and Protein-Solvent interaction

Atanu Maity, Soham Sarkar, Ligesh Theeyancheri +1

Urea at sufficiently high concentration unfolds the secondary structure of proteins leading to denaturation. In contrast, Choline Chloride (ChCl) and urea, in 1:2 molar ratio form…

stat.ME2019★ 1 cited

On Generalizations of Some Distance Based Classifiers for HDLSS Data

Sarbojit Roy, Soham Sarkar, Subhajit Dutta +1

In high dimension, low sample size (HDLSS) settings, classifiers based on Euclidean distances like the nearest neighbor classifier and the average distance classifier perform quite…

stat.ME2018★ 10 cited

On high-dimensional modifications of some graph-based two-sample tests

Soham Sarkar, Rahul Biswas, Anil K. Ghosh

Testing for the equality of two high-dimensional distributions is a challenging problem, and this becomes even more challenging when the sample size is small. Over the last few dec…

stat.ME2016★ 56 cited

On perfect clustering of high dimension, low sample size data

Soham Sarkar, Anil K. Ghosh

Popular clustering algorithms based on usual distance functions (e.g., Euclidean distance) often suffer in high dimension, low sample size (HDLSS) situations, where concentration o…