Evidence map›Paper›PMID 25309806›Full record

ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2012

Optimal Exact Least Squares Rank Minimization.

Shuo Xiang, Yunzhang Zhu, Xiaotong Shen, Jieping Ye

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Shuo XiangDepartment of Computer Science and Engineering, Arizona State University, AZ 85287 ; Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, AZ 85287.
Yunzhang ZhuSchool of Statistics, University of Minnesota, Minneapolis, MN 55455.
Xiaotong ShenSchool of Statistics, University of Minnesota, Minneapolis, MN 55455.
Jieping YeDepartment of Computer Science and Engineering, Arizona State University, AZ 85287 ; Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, AZ 85287.

Funding

Powerful Inference and Prediction for Genetic AssociationR01HL105397 · NHLBI · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2011 to 2018
$2.9M
New Machine Learning Tools for Biomedical DataR01GM081535 · NIGMS · UNIVERSITY OF MINNESOTA · PI PAN, WEI, SHEN, XIAOTONG TOM · 2007 to 2014
$2.2M
Computational Methods for Expression Image AnalysisR01LM010730 · NLM · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI KUMAR, SUDHIR, YE, JIEPING · 2011 to 2013
$934k
NHLBI NIH HHS R01 HL105397NIGMS NIH HHS R01 GM081535NLM NIH HHS R01 LM010730
6 · The paper itself

Abstract

In multivariate analysis, rank minimization emerges when a low-rank structure of matrices is desired as well as a small estimation error. Rank minimization is nonconvex and generally NP-hard, imposing one major challenge. In this paper, we consider a nonconvex least squares formulation, which seeks to minimize the least squares loss function with the rank constraint. Computationally, we develop efficient algorithms to compute a global solution as well as an entire regularization solution path. Theoretically, we show that our method reconstructs the oracle estimator exactly from noisy data. As a result, it recovers the true rank optimally against any method and leads to sharper parameter estimation over its counterpart. Finally, the utility of the proposed method is demonstrated by simulations and image reconstruction from noisy background.

Indexed as

Algorithmsglobal optimalityNonconvexrank minimization

Identifiers

PMID25309806
PMCPMC4191838

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.