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Advances in Intelligent Data Analysis XVII

Overview of attention for book
Cover of 'Advances in Intelligent Data Analysis XVII'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Elements of an Automatic Data Scientist
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    Chapter 2 The Need for Interpretability Biases
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    Chapter 3 Open Data Science
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    Chapter 4 Automatic POI Matching Using an Outlier Detection Based Approach
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    Chapter 5 Fact Checking from Natural Text with Probabilistic Soft Logic
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    Chapter 6 ConvoMap: Using Convolution to Order Boolean Data
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    Chapter 7 Training Neural Networks to Distinguish Craving Smokers, Non-craving Smokers, and Non-smokers
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    Chapter 8 Missing Data Imputation via Denoising Autoencoders: The Untold Story
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    Chapter 9 Online Non-linear Gradient Boosting in Multi-latent Spaces
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    Chapter 10 MDP-based Itinerary Recommendation using Geo-Tagged Social Media
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    Chapter 11 Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization
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    Chapter 12 Non-negative Local Sparse Coding for Subspace Clustering
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    Chapter 13 Pushing the Envelope in Overlapping Communities Detection
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    Chapter 14 Right for the Right Reason: Training Agnostic Networks
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    Chapter 15 Link Prediction in Multi-layer Networks and Its Application to Drug Design
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    Chapter 16 A Hierarchical Ornstein-Uhlenbeck Model for Stochastic Time Series Analysis
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    Chapter 17 Analysing the Footprint of Classifiers in Overlapped and Imbalanced Contexts
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    Chapter 18 Tree-Based Cost Sensitive Methods for Fraud Detection in Imbalanced Data
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    Chapter 19 Reduction Stumps for Multi-class Classification
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    Chapter 20 Decomposition of Quantitative Gaifman Graphs as a Data Analysis Tool
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    Chapter 21 Exploring the Effects of Data Distribution in Missing Data Imputation
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    Chapter 22 Communication-Free Widened Learning of Bayesian Network Classifiers Using Hashed Fiedler Vectors
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    Chapter 23 Expert Finding in Citizen Science Platform for Biodiversity Monitoring via Weighted PageRank Algorithm
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    Chapter 24 Random Forests with Latent Variables to Foster Feature Selection in the Context of Highly Correlated Variables. Illustration with a Bioinformatics Application.
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    Chapter 25 Don’t Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML
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    Chapter 26 Detecting Shifts in Public Opinion: A Big Data Study of Global News Content
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    Chapter 27 Biased Embeddings from Wild Data: Measuring, Understanding and Removing
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    Chapter 28 Real-Time Excavation Detection at Construction Sites using Deep Learning
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    Chapter 29 COBRAS: Interactive Clustering with Pairwise Queries
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    Chapter 30 Automatically Wrangling Spreadsheets into Machine Learning Data Formats
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    Chapter 31 Learned Feature Generation for Molecules
Attention for Chapter 12: Non-negative Local Sparse Coding for Subspace Clustering
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Chapter title
Non-negative Local Sparse Coding for Subspace Clustering
Chapter number 12
Book title
Advances in Intelligent Data Analysis XVII
Published in
arXiv, October 2018
DOI 10.1007/978-3-030-01768-2_12
Book ISBNs
978-3-03-001767-5, 978-3-03-001768-2
Authors

Babak Hosseini, Barbara Hammer, Hosseini, Babak, Hammer, Barbara

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 57%
Student > Doctoral Student 2 29%
Researcher 1 14%
Readers by discipline Count As %
Computer Science 5 71%
Physics and Astronomy 1 14%
Engineering 1 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 14 March 2019.
All research outputs
#15,634,365
of 23,859,750 outputs
Outputs from arXiv
#350,051
of 1,006,649 outputs
Outputs of similar age
#211,576
of 353,530 outputs
Outputs of similar age from arXiv
#11,693
of 27,147 outputs
Altmetric has tracked 23,859,750 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,006,649 research outputs from this source. They receive a mean Attention Score of 4.0. This one has gotten more attention than average, scoring higher than 60% 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 353,530 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 27,147 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.