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- W2945990941 abstract "Most commonly used classification algorithms process data in the form of vectors. At the same time, mod- ern 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." @default.
- W2945990941 created "2019-05-29" @default.
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- W2945990941 date "2019-05-13" @default.
- W2945990941 modified "2023-10-14" @default.
- W2945990941 title "Options for multimodal classification based on L1-Tucker decomposition" @default.
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- W2945990941 doi "https://doi.org/10.1117/12.2520140" @default.
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