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 Xiaodong Feng

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Average citations per article0.78
Citation Count7
Publication count9
Publication years2013-2017
Available for download2
Average downloads per article48.50
Downloads (cumulative)97
Downloads (12 Months)61
Downloads (6 Weeks)8
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9 results found Export Results: bibtexendnoteacmrefcsv

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1 published by ACM
July 2017 ACM Transactions on Intelligent Systems and Technology (TIST) - Survey Paper, Regular Papers and Special Issue: Social Media Processing: Volume 8 Issue 6, September 2017
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 7,   Downloads (12 Months): 53,   Downloads (Overall): 53

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Sparse representation has been a powerful technique for modeling high-dimensional data. As an unsupervised technique to extract sparse representations, sparse coding encodes the original data into a new sparse code space and simultaneously learns a dictionary representing high-level semantics. Existing methods have considered local manifold within high-dimensional data using graph/hypergraph ...
Keywords: Sparse coding, image clustering, multiple-hypergraph learning, image tagging, hypergraph consistency

2
April 2017 Knowledge-Based Systems: Volume 121 Issue C, April 2017
Publisher: Elsevier Science Publishers B. V.
Bibliometrics:
Citation Count: 0

Constructing a graph to represent the structure among data objects plays a fundamental role in various data mining tasks with graph-based learning. Since traditional pairwise distance-based graph construction is sensitive to noise and outliers, sparse representation based graphs (e.g., ź1-graphs) have been proposed in the literature. Although ź1-graphs prove powerful ...
Keywords: Graph construction, Locality preserving, Sparse representation, Graph-based learning

3
June 2016 Engineering Applications of Artificial Intelligence: Volume 52 Issue C, June 2016
Publisher: Pergamon Press, Inc.
Bibliometrics:
Citation Count: 0

Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled ...
Keywords: PHM data challenge, Semi-supervised learning, Non-negative matrix factorization, Maintenance activities identification, Label consistent regularization

4
May 2016 Engineering Applications of Artificial Intelligence: Volume 51 Issue C, May 2016
Publisher: Pergamon Press, Inc.
Bibliometrics:
Citation Count: 1

Social recommendation techniques have been developed to employ user's social connections for both rating prediction and Top-N recommendation. However, they are mostly using social network enhanced matrix factorization (MF) where the objective is to minimize the prediction error of rating scores, which makes it impractical and unsuccessful for Top-N recommendation. ...
Keywords: User modeling, Social network, Local learning, Sparse Linear Model, Top-N recommendation

5
April 2016 PAKDD 2016: Proceedings, Part II, of the 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining - Volume 9652
Publisher: Springer-Verlag New York, Inc.
Bibliometrics:
Citation Count: 0

Sparse representation has been a powerful technique for modeling image data and thus enhance the performance of image clustering. Sparse coding, as an unsupervised way to extract sparse representation, learns a dictionary that represents high-level semantics and the new representations on the dictionary. Though existing sparse coding schemes are considering ...
Keywords: Hypergraph incidence consistency, Image representation, Multiple hypergraph learning, Image clustering, Sparse coding

6
October 2015 KSEM 2015: Proceedings of the 8th International Conference on Knowledge Science, Engineering and Management - Volume 9403
Publisher: Springer-Verlag New York, Inc.
Bibliometrics:
Citation Count: 0

This paper presents a recommendation algorithm based on matrix operations RAMO, which integrates collaborative filtering algorithm with information network-based approach. RAMO exploits information from different objects to increase the recommendation accuracy. Furthermore, a distributed recommendation algorithm DRAMD is proposed based on matrix decomposition using the framework MapReduce. DRAMD can be ...
Keywords: Mapreduce, Matrix decomposition, Recommender system, Distributed computing

7 published by ACM
August 2015 ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 1,   Downloads (12 Months): 8,   Downloads (Overall): 44

Full text available: PDFPDF
Small Group evolution has been of central importance in social sciences and also in the industry for understanding dynamics of team formation. While most of research works studying groups deal at a macro level with evolution of arbitrary size communities, in this paper we restrict ourselves to studying evolution of ...
Keywords: Group Evolution, Higher Order Link Prediction, Hypergraphs, Social Networks, Hypergraph Evolution

8
July 2014 Neurocomputing: Volume 135 Issue C, July 2014
Publisher: Elsevier Science Publishers B. V.
Bibliometrics:
Citation Count: 2

Clustering high-dimensional data has been a challenging problem in data mining and machining learning. Spectral clustering via sparse representation has been proposed for clustering high-dimensional data. A critical stepin spectral clustering is to effectively construct a weight matrix by assessing the proximity between each pair of objects. While sparse representation ...
Keywords: Weight matrix, Sparse representation, High-dimensional data, Spectral clustering

9
October 2013 IDEAL 2013: Proceedings of the 14th International Conference on Intelligent Data Engineering and Automated Learning --- IDEAL 2013 - Volume 8206
Publisher: Springer-Verlag New York, Inc.
Bibliometrics:
Citation Count: 1

Clustering based on sparse representation is an important technique in machine learning and data mining fields. However, it is time-consuming because it constructs l 1 -graph by solving l 1 -minimization with all other samples as dictionary for each sample. This paper is focused on improving the efficiency of clustering ...
Keywords: Weight Matrix, Spectral Clustering, k-nn, Sparse Representation



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