You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Bayesian methods for low-rank matrix estimation: short survey and
theoretical study
|
|---|---|
| Chapter number | 22 |
| Book title |
Algorithmic Learning Theory
|
| Published in |
arXiv, June 2013
|
| DOI | 10.1007/978-3-642-40935-6_22 |
| Book ISBNs |
978-3-64-240934-9, 978-3-64-240935-6
|
| Authors |
Pierre Alquier |
| Editors |
Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 33% |
| Canada | 1 | 17% |
| Unknown | 3 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 2 | 7% |
| Cuba | 2 | 7% |
| Unknown | 25 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 24% |
| Researcher | 5 | 17% |
| Professor | 3 | 10% |
| Student > Doctoral Student | 2 | 7% |
| Other | 2 | 7% |
| Other | 6 | 21% |
| Unknown | 4 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 10 | 34% |
| Computer Science | 7 | 24% |
| Engineering | 4 | 14% |
| Agricultural and Biological Sciences | 1 | 3% |
| Social Sciences | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 5 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 12 July 2016.
All research outputs
#7,240,812
of 22,880,230 outputs
Outputs from arXiv
#159,419
of 939,638 outputs
Outputs of similar age
#63,097
of 197,111 outputs
Outputs of similar age from arXiv
#710
of 8,151 outputs
Altmetric has tracked 22,880,230 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 939,638 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done well, scoring higher than 82% 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 197,111 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 66% of its contemporaries.
We're also able to compare this research output to 8,151 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.