8 citations · 19 across the 5 of their papers we have counts for
9 papers
Clustering of Data with Missing Entries
Sunrita Poddar, Mathews Jacob
The analysis of large datasets is often complicated by the presence of missing entries, mainly because most of the current machine learning algorithms are designed to work with ful…
Recovery of Point Clouds on Surfaces: Application to Image Reconstruction
Sunrita Poddar, Mathews Jacob
We introduce a framework for the recovery of points on a smooth surface in high-dimensional space, with application to dynamic imaging. We assume the surface to be the zero-level s…
Separation-Free Super-Resolution from Compressed Measurements is Possible: an Orthonormal Atomic Norm Minimization Approach
Weiyu Xu, Jirong Yi, Soura Dasgupta +3
We consider the problem of recovering the superposition of distinct complex exponential functions from compressed non-uniform time-domain samples. Total Variation (TV) minimiza…
Clustering of Data with Missing Entries using Non-convex Fusion Penalties
Sunrita Poddar, Mathews Jacob
The presence of missing entries in data often creates challenges for pattern recognition algorithms. Traditional algorithms for clustering data assume that all the feature values a…
Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series
Arvind Balachandrasekaran, Mathews Jacob
We propose a structured low rank matrix completion algorithm to recover a time series of images consisting of linear combination of exponential parameters at every pixel, from unde…
Structured low-rank recovery of piecewise constant signals with performance guarantees
Greg Ongie, Sampurna Biswas, Mathews Jacob
We derive theoretical guarantees for the exact recovery of piecewise constant two-dimensional images from a minimal number of non-uniform Fourier samples using a convex matrix comp…