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• W398889120 abstract "In this paper we propose a method for output dimensionality reduction based on dictionary learning. Our final goal is the prediction of complete time series from standard input vectorial data. To do so we formulate a single learning problem which on the one hand learns a new representation of the output space, using dictionary learning, and reduces its dimension, while on the other hand learns to predict from the input data the new output representation, using standard multi-output regression. As a result the choice the new representation is informed by the prediction error, and the regression model by the reconstruction error. We provide preliminary experiments which demonstrate the potential of our approach and discuss its limitations." @default.
• W398889120 created "2016-06-24" @default.
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• W398889120 date "2013-01-01" @default.
• W398889120 modified "2023-09-24" @default.
• W398889120 title "Dictionary-based output-space dimensionality reduction" @default.
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