Prerna Khurana

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In this work we propose a classification framework called class-wise deep dictionary learning (CWDDL). For each class, multiple levels of dictionaries are learnt using features from the previous level as inputs (for first level the input is the raw training sample). It is assumed that the cascaded dictionaries form a basis for expressing test samples for(More)
This work addresses the problem of estimating T2 maps from very few (two) echoes. Existing multi-parametric non-linear curve fitting techniques require a large number (16 or 32) of echoes to estimate T2 values. We show that our method yields very accurate and robust results from only two echoes, where as the curve-fitting techniques require about 16 echoes(More)
Algorithms for sparse recovery problems from non-linear measurements have attracted some attention lately. Closely related to the problem of sparse is recovery is the problem of low-rank matrix recovery. There is no work on the topic of low-rank matrix recovery from non-linear measurements. This is the first study that proposes two algorithms for the said(More)
The deployment of smart meters by utilities holds the promise of improvements in operational efficiency, reliability and cost savings. With power measurements from smart meters, utilities can deploy innovative programs that allow end users to better control their energy usage while simultaneously reducing peak demand across the grid. In this paper, to(More)
In this work we propose a new framework for combined feature extraction and classification. The base idea stems from the sparse representation based classification; where in the training samples from each class are assumed to form a basis for representing the same. Later studies learned a basis for each class using dictionary learning; these were shallow(More)
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