Recursive Least Squares Dictionary Learning Algorithm

By Karl Skretting, Kjersti Engan. IEEE Transactions on Signal Processing, 2010

397 citations recorded by the source corpus.

Subfields: Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a27e8212035e26e68128c893e5b0e0148ddd177d

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In Computational Mechanics

In Signal Processing

Citations in the corpus

The Cosparse Analysis Model and Algorithms (2011, neutral): “…these algorithms’ performance guarantees (e.g. [25, 16, 41, 42, 2]), addressing ways to obtain the dictionary from a bulk of data (e.g. [22, 1, 30, 38]), and beyond all these, attacking a long series of applications in signal and image processing with this model, demonstrating often…”
Sparse Modeling for Image and Vision Processing (2014, methodology): “Note that along the same lines, a similar algorithm called “recursive least square dictionary learning” was independently developed by Skretting and Engan [2010] focusing on the `0-penalty.”
Dictionaries for Sparse Representation Modeling (2010, background): “do not discuss the topic further) is the recent advancement in online dictionary learning [76], [77], which allows training dictionaries from very large sets of examples, and is found to accelerate convergence and improve the trained result.”
On the Identifiability of Overcomplete Dictionaries via the Minimisation Principle Underlying K-SVD (2013, background): “…the research focus in dictionary learning has been on algorithmic development, meaning that by now there are several dictionary learning algorithms, which are efﬁcient in practice and therefore popular in applications, see [13], [19], [1], [22], [34], [20], [29] or [26] for a more complete survey.”
Pedestrian Parsing via Deep Decompositional Network (2013, methodology): “In the case when the data set is very large and it is hard to compute the matrix inversion, one can employ the on-line dictionary learning algorithm [23] instead of the closed form solutions to recursively update the weight matrices.”