Learning optimally separated class-specific subspace representations using convolutional autoencoder
In this work, we propose a novel convolutional autoencoder based architecture to generate subspace specific feature representations that are best suited for classification task. The class-specific data is assumed to lie in low dimensional linear subspaces, which could be noisy and not well separated...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
18.05.2021
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Subjects | |
Online Access | Get full text |
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