---
abstract: "In order to perform object recognition it is necessary to learn representations of the underlying components of images. Such components correspond to objects, object-parts, or features. Non-negative matrix factorisation is a generative model that has been specifically proposed for finding such meaningful representations of image data, through the use of non-negativity constraints on the factors. This article reports on an empirical investigation of the performance of non-negative matrix factorisation algorithms. It is found that such algorithms need to impose additional constraints on the sparseness of the factors in order to successfully deal with occlusion. However, these constraints can themselves result in these algorithms failing to identify image components under certain conditions. In contrast, a recognition model (a competitive learning neural network algorithm) reliably and accurately learns representations of elementary image features without such constraints.\n"
altloc:
- http://www.cbcd.bbk.ac.uk/people/mike/Doc/jmlr06.pdf
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creators_id: []
creators_name:
- family: Spratling
given: Michael W
honourific: ''
lineage: ''
date: 2006
date_type: published
datestamp: 2006-05-25
department: ~
dir: disk0/00/00/48/86
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eprint_status: archive
eprintid: 4886
fileinfo: /style/images/fileicons/application_pdf.png;/4886/1/jmlr06.pdf
full_text_status: public
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ispublished: pub
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keywords: non-negative matrix factorisation; competitive learning; dendritic inhibition; object recognition
lastmod: 2011-03-11 08:56:26
latitude: ~
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metadata_visibility: show
note: ~
number: ~
pagerange: 793-815
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publication: Journal of Machine Learning Research
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refereed: TRUE
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reportno: ~
rev_number: 12
series: ~
source: ~
status_changed: 2007-09-12 17:03:20
subjects:
- comp-sci-mach-vis
- comp-sci-mach-learn
- comp-sci-neural-nets
succeeds: ~
suggestions: ~
sword_depositor: ~
sword_slug: ~
thesistype: ~
title: Learning image components for object recognition
type: journalp
userid: 1040
volume: 7