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Machine Learning and Knowledge Discovery in Databases

Overview of attention for book
Cover of 'Machine Learning and Knowledge Discovery in Databases'

Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 Common Substructure Learning of Multiple Graphical Gaussian Models
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    Chapter 2 Mining Research Topic-Related Influence between Academia and Industry
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    Chapter 3 Typology of Mixed-Membership Models: Towards a Design Method
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    Chapter 4 ShiftTree: An Interpretable Model-Based Approach for Time Series Classification
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    Chapter 5 Image Classification for Age-related Macular Degeneration Screening Using Hierarchical Image Decompositions and Graph Mining
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    Chapter 6 Online Structure Learning for Markov Logic Networks
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    Chapter 7 Fourier-Information Duality in the Identity Management Problem
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    Chapter 8 Eigenvector Sensitive Feature Selection for Spectral Clustering
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    Chapter 9 Restricted Deep Belief Networks for Multi-view Learning
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    Chapter 10 Motion Segmentation by Model-Based Clustering of Incomplete Trajectories
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    Chapter 11 PTMSearch: A Greedy Tree Traversal Algorithm for Finding Protein Post-Translational Modifications in Tandem Mass Spectra
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    Chapter 12 Efficient Mining of Top Correlated Patterns Based on Null-Invariant Measures
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    Chapter 13 Smooth Receiver Operating Characteristics ( smROC ) Curves
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    Chapter 14 A Boosting Approach to Multiview Classification with Cooperation
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    Chapter 15 ARTEMIS: Assessing the Similarity of Event-Interval Sequences
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    Chapter 16 Unifying Guilt-by-Association Approaches: Theorems and Fast Algorithms
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    Chapter 17 Online Clustering of High-Dimensional Trajectories under Concept Drift
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    Chapter 18 Gaussian Logic for Predictive Classification
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    Chapter 19 Toward a Fair Review-Management System
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    Chapter 20 Focused Multi-task Learning Using Gaussian Processes
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    Chapter 21 Reinforcement Learning through Global Stochastic Search in N-MDPs
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    Chapter 22 Analyzing Word Frequencies in Large Text Corpora Using Inter-arrival Times and Bootstrapping
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    Chapter 23 Discovering Temporal Bisociations for Linking Concepts over Time
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    Chapter 24 Minimum Neighbor Distance Estimators of Intrinsic Dimension
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    Chapter 25 Graph Evolution via Social Diffusion Processes
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    Chapter 26 Multi-Subspace Representation and Discovery
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    Chapter 27 A Novel Stability Based Feature Selection Framework for k-means Clustering
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    Chapter 28 Link Prediction via Matrix Factorization
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    Chapter 29 On Oblique Random Forests
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    Chapter 30 An Alternating Direction Method for Dual MAP LP Relaxation
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    Chapter 31 Aggregating Independent and Dependent Models to Learn Multi-label Classifiers
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    Chapter 32 Tensor Factorization Using Auxiliary Information
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    Chapter 33 Kernels for Link Prediction with Latent Feature Models
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    Chapter 34 Frequency-Aware Truncated Methods for Sparse Online Learning
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    Chapter 35 A Shapley Value Approach for Influence Attribution
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    Chapter 36 Fast Approximate Text Document Clustering Using Compressive Sampling
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    Chapter 37 Ancestor Relations in the Presence of Unobserved Variables
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    Chapter 38 ShareBoost: Boosting for Multi-view Learning with Performance Guarantees
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    Chapter 39 Analyzing and Escaping Local Optima in Planning as Inference for Partially Observable Domains
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    Chapter 40 Abductive Plan Recognition by Extending Bayesian Logic Programs
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    Chapter 41 Higher Order Contractive Auto-Encoder
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    Chapter 42 The VC-Dimension of SQL Queries and Selectivity Estimation through Sampling
Attention for Chapter 32: Tensor Factorization Using Auxiliary Information
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

15 tweeters


1 Dimensions

Readers on

5 Mendeley
1 CiteULike
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Chapter title
Tensor Factorization Using Auxiliary Information
Chapter number 32
Book title
Machine Learning and Knowledge Discovery in Databases
Published in
Lecture notes in computer science, January 2011
DOI 10.1007/978-3-642-23783-6_32
Book ISBNs
978-3-64-223782-9, 978-3-64-223783-6

Atsuhiro Narita, Kohei Hayashi, Ryota Tomioka, Hisashi Kashima, Narita, Atsuhiro, Hayashi, Kohei, Tomioka, Ryota, Kashima, Hisashi

Twitter Demographics

The data shown below were collected from the profiles of 15 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 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 60%
Student > Bachelor 2 40%
Student > Doctoral Student 2 40%
Student > Master 1 20%
Student > Ph. D. Student 1 20%
Other 0 0%
Readers by discipline Count As %
Computer Science 4 80%
Materials Science 2 40%
Neuroscience 1 20%
Chemistry 1 20%
Engineering 1 20%
Other 0 0%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 01 September 2011.
All research outputs
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Outputs from Lecture notes in computer science
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Outputs of similar age
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Outputs of similar age from Lecture notes in computer science
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Altmetric has tracked 12,377,816 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,236 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done particularly well, scoring higher than 95% 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 91,884 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 63 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 98% of its contemporaries.