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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 techniques where only one level of dictionary...
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 that class. Based on this assumption sparse...
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 develop data analysis tools for applications enabling...
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