Greedy Deep Dictionary Learning

Greedy Deep Dictionary Learning. Tariyal, S., Majumdar, A., Singh, R., & Vatsa, M. January, 2016. arXiv:1602.00203 [cs, stat]

Paper doi abstract bibtex

In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning tools like discriminative KSVD and label consistent KSVD. Our method yields better results than all.

@misc{tariyal_greedy_2016,
	title = {Greedy {Deep} {Dictionary} {Learning}},
	url = {http://arxiv.org/abs/1602.00203},
	doi = {10.48550/arXiv.1602.00203},
	abstract = {In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning tools like discriminative KSVD and label consistent KSVD. Our method yields better results than all.},
	language = {en},
	urldate = {2023-07-04},
	publisher = {arXiv},
	author = {Tariyal, Snigdha and Majumdar, Angshul and Singh, Richa and Vatsa, Mayank},
	month = jan,
	year = {2016},
	note = {arXiv:1602.00203 [cs, stat]},
	keywords = {\#Deep Learning, /unread, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
}

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