Autoencoders Learning Sparse Representation

dc.contributor.author	Sharma A.; Gupta R.
dc.date.accessioned	2025-05-23T11:23:36Z
dc.description.abstract	Many regularized autoencoders learn a sparse rep-resentation of data. This type of representation enhances robust-ness against noise and computational efficiencies. Our objective in this paper is to provide the conditions under which sparsity is encouraged by AE under a little less restrictive view of data. We have shown a relaxed observed representation of input data and given the conditions on AE to promote sparsity. © 2022 IEEE.
dc.identifier.doi	https://doi.org/10.1109/OCIT56763.2022.00017
dc.identifier.uri	http://172.23.0.11:4000/handle/123456789/9193
dc.relation.ispartofseries	Proceedings - 2022 OITS International Conference on Information Technology, OCIT 2022
dc.title	Autoencoders Learning Sparse Representation

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