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• W2553683256 abstract "As an unsupervised learning method, sparse coding can discover high-level representations for an input in a large variety of learning problems. Under semi-supervised settings, sparse coding is used to extract features for a supervised task such as classification. While sparse representations learned from unlabeled data independently of the supervised task perform well, we argue that sparse coding should also be built as a holistic learning unit optimizing on the supervised task objectives more explicitly. In this paper, we propose sparse-coded net, a feedforward model that integrates sparse coding and task-driven output layers, and describe training methods in detail. After pretraining a sparse-coded net via semi-supervised learning, we optimize its task-specific performance in a novel backpropagation algorithm that can traverse nonlinear feature pooling operators to update the dictionary. Thus, sparse-coded net can be applied to supervised dictionary learning. We evaluate sparse-coded net with classification problems in sound, image, and text data. The results confirm a significant improvement over semi-supervised learning as well as superior classification performance against deep stacked autoencoder neural network and GMM-SVM pipelines in small to medium-scale settings." @default.
• W2553683256 created "2016-11-30" @default.
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• W2553683256 date "2016-09-01" @default.
• W2553683256 modified "2023-09-23" @default.
• W2553683256 title "Sparse-coded net model and applications" @default.
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• W2553683256 doi "https://doi.org/10.1109/mlsp.2016.7738828" @default.
• W2553683256 hasPublicationYear "2016" @default.
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