Search Results for author: Monami Banerjee

Found 8 papers, 2 papers with code

VolterraNet: A higher order convolutional network with group equivariance for homogeneous manifolds

1 code implementation5 Jun 2021 Monami Banerjee, Rudrasis Chakraborty, Jose Bouza, Baba C. Vemuri

In this paper, we present a novel higher order Volterra convolutional neural network (VolterraNet) for data defined as samples of functions on Riemannian homogeneous spaces.

Translation

A CNN for homogneous Riemannian manifolds with applications to Neuroimaging

no code implementations14 May 2018 Rudrasis Chakraborty, Monami Banerjee, Baba C. Vemuri

(ii) As a corrolary, we prove the equivariance of the correlation operation to group actions admitted by the input domains which are Riemannian homogeneous manifolds.

Dictionary Learning and Sparse Coding on Statistical Manifolds

no code implementations3 May 2018 Rudrasis Chakraborty, Monami Banerjee, Baba C. Vemuri

In this paper, we propose a novel information theoretic framework for dictionary learning (DL) and sparse coding (SC) on a statistical manifold (the manifold of probability distributions).

Dictionary Learning General Classification

Sparse Exact PGA on Riemannian Manifolds

no code implementations ICCV 2017 Monami Banerjee, Rudrasis Chakraborty, Baba C. Vemuri

In this paper, we present a novel generalization of SPCA, called sparse exact PGA (SEPGA) that can cope with manifold-valued input data and respect the intrinsic geometry of the underlying manifold.

Computational Efficiency Dimensionality Reduction

A Nonlinear Regression Technique for Manifold Valued Data With Applications to Medical Image Analysis

no code implementations CVPR 2016 Monami Banerjee, Rudrasis Chakraborty, Edward Ofori, Michael S. Okun, David E. Viallancourt, Baba C. Vemuri

With the exception of a few, most existing methods of regression for manifold valued data are limited to geodesic regression which is a generalization of the linear regression in vector-spaces.

regression

An information theoretic formulation of the Dictionary Learning and Sparse Coding Problems on Statistical Manifolds

no code implementations23 Apr 2016 Rudrasis Chakraborty, Monami Banerjee, Victoria Crawford, Baba C. Vemuri

In this work, we propose a novel information theoretic framework for dictionary learning (DL) and sparse coding (SC) on a statistical manifold (the manifold of probability distributions).

Dictionary Learning General Classification

Interpolation on the Manifold of K Component GMMs

no code implementations ICCV 2015 Hyunwoo J. Kim, Nagesh Adluru, Monami Banerjee, Baba C. Vemuri, Vikas Singh

Probability density functions (PDFs) are fundamental "objects" in mathematics with numerous applications in computer vision, machine learning and medical imaging.

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