Abstract Details
Activity Number:
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582
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Type:
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Invited
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Date/Time:
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Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #310735
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View Presentation
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Title:
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MrNMF and SrNMF: Recent Development in Robust Non-Negative Matrix Factorization Procedures
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Author(s):
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Yifan Xu*+ and Jiayang Sun and Kenneth K. Lopiano and S. Stanley Young
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Companies:
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Case Western Reserve University and Case Western Reserve University and SAMSI and NISS
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Keywords:
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Robust non-negative matrix factorization ;
Principle component analysis ;
Sparsity ;
Supervised learning ;
Big data
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Abstract:
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PCA is one of the most widely used multivariate analysis techniques, modernized for analyzing large and sparse data. Nonnegative Matrix Factorization (NMF) is a modern alternative to PCA, offering better parts-based decompositions. Both procedures can fail if the noises or outliers in a data set are ignored. In this talk we show our recent development in robust NMF (rNMF) procedure for analyzing large and corrupted data. Our rNMF algorithm solves a penalized trimmed criterion iteratively, with sparsity controls and multiple trimming mechanisms for handling different types of data. The convergence of the algorithm is shown. Its associated sequential plots offer a new way of selecting the critical dimension used in NMF; and facilitates interpretation of the factorization. We compare rNMF with robust PCA/SVD and regular NMF procedures, and demonstrate the power of rNMF and its sibling, MrNMF (that allows semi-supervised intelligence controls) for removing various corruptions, data cleaning, compression, feature selection, and clustering. If time permits, we show an application to ovarian cancer data, and SrNMF, a new variant incorporating a kernel machine for supervised learning.
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Authors who are presenting talks have a * after their name.
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