NDLI: Limitations of matrix completion via trace norm minimization

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Volume 12, Issue 2, December 2010

On the importance of sharing negative results

ISI-KDD

Topic-based social network analysis for virtual communities of interests in the dark web

Latent semantic indexing (LSI) fails for TREC collections

Tenth international workshop on multimedia data mining

Activity recognition using cell phone accelerometers

On cross-validation and stacking

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Fully decentralized computation of aggregates over data streams

Limitations of matrix completion via trace norm minimization

Knowledge discovery from sensor data (SensorKDD)

A framework for mining interesting pattern sets

On exploiting hierarchical label structure with pairwise classifiers

Novel data stream pattern mining report on the StreamKDD'10 workshop

Unexpected results in automatic list extraction on the web

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Unexpected results in online controlled experiments

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Volume 12, Issue 1, June 2010

Limitations of matrix completion via trace norm minimization

Content Provider	ACM Digital Library
Author	Yu, Philip S. Shi, Xiaoxiao
Abstract	In recent years, compressive sensing attracts intensive attentions in the field of statistics, automatic control, data mining and machine learning. It assumes the sparsity of the dataset and proposes that the whole dataset can be reconstructed by just observing a small set of samples. One of the important approaches of compressive sensing is trace norm minimization, which can minimize the rank of the data matrix under some conditions. For example, in collaborative filtering, we are given a small set of observed item ratings of some users and we want to predict the missing values in the rating matrix. It is assumed that the users' ratings are affected by only a few factors and the resulting rating matrix should be of low rank. In this paper, we analyze the issues related to trace norm minimization and find an unexpected result that trace norm minimization often does not work as well as expected.
Starting Page	16
Ending Page	20
Page Count	5
File Format	PDF
ISSN	19310145 19310153
DOI	10.1145/1964897.1964902
Journal	ACM SIGKDD Explorations Newsletter (SKDD)
Volume Number	12
Issue Number	2
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2000-06-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization

On Tensor Completion via Nuclear Norm Minimization

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Trace Norm Regularized Matrix Factorization for Service Recommendation

Variational Bayesian View of Weighted Trace Norm Regularization for Matrix Factorization

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