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Fast dictionary learning from incomplete data

Overview of attention for article published in EURASIP Journal on Advances in Signal Processing, February 2018
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About this Attention Score

  • Among the highest-scoring outputs from this source (#46 of 315)
  • Above-average Attention Score compared to outputs of the same age (63rd percentile)

Mentioned by

twitter
9 tweeters

Citations

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4 Dimensions

Readers on

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16 Mendeley
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Title
Fast dictionary learning from incomplete data
Published in
EURASIP Journal on Advances in Signal Processing, February 2018
DOI 10.1186/s13634-018-0533-0
Pubmed ID
Authors

Valeriya Naumova, Karin Schnass

Abstract

This paper extends the recently proposed and theoretically justified iterative thresholding and K residual means (ITKrM) algorithm to learning dictionaries from incomplete/masked training data (ITKrMM). It further adapts the algorithm to the presence of a low-rank component in the data and provides a strategy for recovering this low-rank component again from incomplete data. Several synthetic experiments show the advantages of incorporating information about the corruption into the algorithm. Further experiments on image data confirm the importance of considering a low-rank component in the data and show that the algorithm compares favourably to its closest dictionary learning counterparts, wKSVD and BPFA, either in terms of computational complexity or in terms of consistency between the dictionaries learned from corrupted and uncorrupted data. To further confirm the appropriateness of the learned dictionaries, we explore an application to sparsity-based image inpainting. There the ITKrMM dictionaries show a similar performance to other learned dictionaries like wKSVD and BPFA and a superior performance to other algorithms based on pre-defined/analytic dictionaries.

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 31%
Researcher 3 19%
Student > Doctoral Student 3 19%
Student > Bachelor 1 6%
Lecturer 1 6%
Other 3 19%
Readers by discipline Count As %
Computer Science 5 31%
Engineering 4 25%
Physics and Astronomy 1 6%
Economics, Econometrics and Finance 1 6%
Earth and Planetary Sciences 1 6%
Other 2 13%
Unknown 2 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 February 2018.
All research outputs
#4,444,464
of 14,557,796 outputs
Outputs from EURASIP Journal on Advances in Signal Processing
#46
of 315 outputs
Outputs of similar age
#118,900
of 348,219 outputs
Outputs of similar age from EURASIP Journal on Advances in Signal Processing
#1
of 1 outputs
Altmetric has tracked 14,557,796 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 315 research outputs from this source. They receive a mean Attention Score of 2.9. This one has gotten more attention than average, scoring higher than 53% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 348,219 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them