Joint NMF for Hybrid Clustering based on Content and Connection ...
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Joint NMF for Hybrid Clustering based on Content and Connection Structure Rundong Du1 , Barry Drake2,3 and Haesun Park2 1. School of Mathematics 2. School of Computational Science and Engineering 3. Georgia Tech Research Institute
Georgia Institute of Technology, Atlanta, GA SIAM ALA HKBU, May, 2018
Supported in part by Rundong Du, Barry Drake and Haesun Park
Hybrid Clustering with NMF
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Constrained Low Rank Approximations for Scalable Data Analytics Objectives: Model text and graph clustering problems Design, verify, and deploy scalable numerical alg. and software Develop divide-and-conquer methods to handle problems of larger size for various computing environments Goal: Orders of magnitude speed improvements over existing data analytics methods and solutions of higher quality
Rundong Du, Barry Drake and Haesun Park
Why CLRA ? Utilize advances in numerical linear algebra and optimization Exploit software such as BLAS and LAPACK Behavior of algorithms easier to analyze Facilitates design of MPI based algorithms for scalable solutions Can easily be modified for various problem demands, e.g. adaptive methods
Hybrid Clustering with NMF
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Clustering: data clustering, topic modeling, graph clustering, community detection, hybrid clustering... Vector space
