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Learning to classify with missing and corrupted features

Overview of attention for article published in Machine Learning, July 2009
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)

Mentioned by

patent
1 patent
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
78 Dimensions

Readers on

mendeley
142 Mendeley
Title
Learning to classify with missing and corrupted features
Published in
Machine Learning, July 2009
DOI 10.1007/s10994-009-5124-8
Authors

Ofer Dekel, Ohad Shamir, Lin Xiao

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 1%
France 1 <1%
Austria 1 <1%
Australia 1 <1%
Canada 1 <1%
Mexico 1 <1%
Denmark 1 <1%
China 1 <1%
Russia 1 <1%
Other 2 1%
Unknown 130 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 47 33%
Student > Master 27 19%
Researcher 17 12%
Professor > Associate Professor 10 7%
Student > Bachelor 7 5%
Other 19 13%
Unknown 15 11%
Readers by discipline Count As %
Computer Science 89 63%
Engineering 19 13%
Mathematics 5 4%
Medicine and Dentistry 2 1%
Chemistry 2 1%
Other 3 2%
Unknown 22 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 18 June 2021.
All research outputs
#4,696,781
of 22,790,780 outputs
Outputs from Machine Learning
#131
of 961 outputs
Outputs of similar age
#20,060
of 110,033 outputs
Outputs of similar age from Machine Learning
#2
of 4 outputs
Altmetric has tracked 22,790,780 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 961 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done well, scoring higher than 81% 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 110,033 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 73% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.