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Creators/Authors contains: "Dhanaraj, Mayur"

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  1. ; ; ; (, IEEE Journal of Selected Topics in Signal Processing)
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  2. ; ; ; ; ; ; (, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
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  3. ; (, IEEE Global Conference on Information Processing)
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  4. ; ; ; ; (, SPIE DCS 2019, vol. 10989)
    Most commonly used classification algorithms process data in the form of vectors. At the same time, modern datasets often comprise multimodal measurements that are naturally modeled as multi-way arrays, also known as tensors. Processing multi-way data in their tensor form can enable enhanced inference and classification accuracy. Tucker decomposition is a standard method for tensor data processing, which however has demonstrated severe sensitivity to corrupted measurements due to its L2-norm formulation. In this work, we present a selection of classification methods that employ an L1-norm-based, corruption-resistant reformulation of Tucker (L1-Tucker). Our experimental studies on multiple real datasets corroborate the corruption-resistance and classification accuracy afforded by L1-Tucker. 
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