Change search
ReferencesLink to record
Permanent link

Direct link
Dictionary Learning with Equiprobable Matching Pursuit
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-5662-825X
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
Number of Authors: 2
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Sparse signal representations based on linear combinations of learned atomshave been used to obtain state-of-the-art results in several practical signalprocessing applications. Approximation methods are needed to processhigh-dimensional signals in this way because the problem to calculate optimalatoms for sparse coding is NP-hard. Here we study greedy algorithms forunsupervised learning of dictionaries of shift-invariant atoms and propose anew method where each atom is selected with the same probability on average,which corresponds to the homeostatic regulation of a recurrent convolutionalneural network. Equiprobable selection can be used with several greedyalgorithms for dictionary learning to ensure that all atoms adapt duringtraining and that no particular atom is more likely to take part in the linearcombination on average. We demonstrate via simulation experiments thatdictionary learning with equiprobable selection results in higher entropy ofthe sparse representation and lower reconstruction and denoising errors, bothin the case of ordinary matching pursuit and orthogonal matching pursuit withshift-invariant dictionaries. Furthermore, we show that the computational costsof the matching pursuits are lower with equiprobable selection, leading tofaster and more accurate dictionary learning algorithms.

Keyword [en]
dictionary learning, sparse approximation, matching pursuit, unsupervised learning, homeostatic regulation, neuromorphic engineering
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics
Identifiers
URN: urn:nbn:se:ltu:diva-60768OAI: oai:DiVA.org:ltu-60768DiVA: diva2:1050435
Funder
The Kempe Foundations
Available from: 2016-11-29 Created: 2016-11-29 Last updated: 2016-12-01

Open Access in DiVA

fulltext(438 kB)5 downloads
File information
File name FULLTEXT01.pdfFile size 438 kBChecksum SHA-512
52a2b35250cd030008772a766300bfe347c952e19c3b8acbcb25e04e4252d6ca1373e4ac536caf8037ebd5adaac5898e8007b224fd78e1aafb79536bc31fa7b7
Type fulltextMimetype application/pdf

Other links

http://arxiv.org/abs/1611.09333

Search in DiVA

By author/editor
Sandin, FredrikMartin del Campo Barraza, Sergio
By organisation
Embedded Internet Systems Lab
Other Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 5 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

ReferencesLink to record
Permanent link

Direct link