Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/56571
Type: Report
Title: Recovering the missing components in a large noisy low-rank matrix: Application to sfm
Author: Chen, Pei
Suter, David
Publisher: Monash University
Issue Date: 2003
Series/Report no.: Technical report ; MECSE-25-2003
School/Discipline: School of Computer Science
Statement of
Responsibility: 
Pei Chen and David Suter
Abstract: In computer vision, it is common to require operations on matrices with “missing data”, for example because of occlusion or tracking failures in the Structure from Motion (SFM) problem. Such a problem can be tackled, allowing the recovery of the missing values, if the matrix should be of low rank (when noise free). The filling in of missing values is known as imputation. Imputation can also be applied in the various subspace techniques for face and shape classification, on-line “recommender” systems, and a wide variety of other applications. However, iterative imputation can lead to the “recovery” of data that is seriously in error. In this paper we provide a method to recover the most reliable imputation, in terms of deciding when the inclusion of extra rows or columns, containing significant numbers of missing entries, is likely to lead to poor recovery of the missing parts. Although the proposed approach can be equally applied to a wide range of imputation methods, this paper addresses only the SFM problem. The performance of the proposed method is compared with Jacobs’ and Shum’s methods for SFM
Keywords: Imputation; Missing-data problem; Rank constraint; Singular value decomposition; Denoising capacity; Structure from motion; Affine SFM; Linear subspace
RMID: 0020094192
Appears in Collections:Computer Science publications

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