NSF Award Search: Award # 0939187

Award Abstract # 0939187

EAGER: Collaborative Research: Cross-Domain Knowledge Transformation via Matrix Decompositions

NSF Org:	CCF Division of Computing and Communication Foundations
Recipient:	UNIVERSITY OF TEXAS AT ARLINGTON
Initial Amendment Date:	September 1, 2009
Latest Amendment Date:	September 1, 2009
Award Number:	0939187
Award Instrument:	Standard Grant
Program Manager:	Petros Drineas CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering
Start Date:	September 1, 2009
End Date:	August 31, 2010 (Estimated)
Total Intended Award Amount:	$53,855.00
Total Awarded Amount to Date:	$53,855.00
Funds Obligated to Date:	FY 2009 = $53,855.00
History of Investigator:	Chris Ding (Principal Investigator) chqding@uta.edu Heng Huang (Co-Principal Investigator)
Recipient Sponsored Research Office:	University of Texas at Arlington 701 S NEDDERMAN DR ARLINGTON TX US 76019-9800 (817)272-2105
Sponsor Congressional District:	25
Primary Place of Performance:	University of Texas at Arlington 701 S NEDDERMAN DR ARLINGTON TX US 76019-9800
Primary Place of Performance Congressional District:	25
Unique Entity Identifier (UEI):	LMLUKUPJJ9N3
Parent UEI:
NSF Program(s):	Info Integration & Informatics, NUM, SYMBOL, & ALGEBRA COMPUT
Primary Program Source:	01000910DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	HPCC, 7916, 9218
Program Element Code(s):	736400, 793300
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

EAGER: Collaborative Research: Cross-domain Knowledge Transformation via Matrix Decompositions

Traditional data mining algorithms discover knowledge in new domains starting from the scratch, ignoring knowledge learned in other domains. Knowledge transformation is a transformative paradigm that utilizes previously acquired knowledge in other domains to guide knowledge discovery process in a new domain and is especially useful for large data sets. In particular, utilizing applicable knowledge in other domains helps to stabilize the unsupervised learning and generate results that we may have preliminary understanding.

The goal of this project is to design and develop cross-domain knowledge transformation mechanisms for knowledge discovery. The transformation mechanisms are based on matrix decompositions where the knowledge been transferred are represented directly and explicitly ? making them easy to comprehend and be utilized in practice. The proposed mechanisms provide a versatile knowledge transformation framework with solid theoretical foundation and enable a new paradigm of unsupervised learning with domain knowledge.

The usefulness of these knowledge transformation mechanisms/systems will be demonstrated for effective information retrieval, consumer recommender systems, and product/online opinion sentiment analysis. The versatility of this transformative metholody will be verified across many domains.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Chris Ding, Tao Li, and Michael I. Jordan "Convex and Semi-Nonnegative Matrix Factorizations" IEEE Transactions on Pattern Analysis and Machine Intelligence , v.32(1) , 2010 , p.45

Dijun Luo, Chris Ding, Heng Huang, Tao Li "Non-negative Laplacian Embedding" IEEE International Conference on Data Mining (ICDM 2009) , 2009 , p.337

Dijun Luo, Heng Huang, Chris Ding, Feiping Nie "On The Eigenvectors of p-Laplacian" Machine Learning , v.81(1) , 2010 , p.37

Fei Wang, Chris Ding, and Tao Li "Integrated KL (K-means - Laplacian) Clustering: A New Clustering Approach by Combining Attribute Data and Pairwise Relations" Siam Data Mining , 2009 , p.38

Hua Wang, Heng Huang, Chris Ding "Discriminant Laplacian Embedding" Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2010) , 2010 , p.618

Quanquan Gu, Jie Zhou, Chris Ding "Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs" Siam Data Mining , 2010 , p.199

Tao Li, Vikas Sindhwani, Chris Ding, and Yi Zhang "Bridging Domains with Words: Opinion Analysis with Matrix Tri-factorizations" Siam Data Mining , 2010 , p.293

Tao Li, Vikas Sindhwani, Chris Ding, and Yi Zhang "Knowledge Transformation for Cross-domain Sentiment Classification" Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval , 2009 , p.716

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