---
abstract: "Many existing independent component analysis algorithms include a preprocessing stage where the inputs are sphered. This amounts to normalising the data such that all correlations between the variables are removed. In this work, I show that sphering allows very weak contextual modulation to steer the development of meaningful features. Context-biased competition has been proposed as a model of covert attention and I propose that sphering-like normalisation also allows weaker top-down bias to guide attention.\n"
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creators_name:
- family: Valpola
given: Harri
honourific: ''
lineage: ''
date: 2004-05
date_type: published
datestamp: 2004-05-14
department: Artificial Intelligence Laboratory
dir: disk0/00/00/36/33
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eprint_status: archive
eprintid: 3633
fileinfo: /style/images/fileicons/application_pdf.png;/3633/1/tr04a.pdf
full_text_status: public
importid: ~
institution: University of Zurich
isbn: ~
ispublished: pub
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keywords: ~
lastmod: 2011-03-11 08:55:36
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metadata_visibility: show
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pubdom: FALSE
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refereed: FALSE
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relation_type: []
relation_uri: []
reportno: ~
rev_number: 12
series: ~
source: ~
status_changed: 2007-09-12 16:52:28
subjects:
- comp-neuro-sci
- comp-sci-mach-learn
- comp-sci-neural-nets
- comp-sci-art-intel
succeeds: ~
suggestions: ~
sword_depositor: ~
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thesistype: ~
title: Behaviourally meaningful representations from normalisation and context-guided denoising
type: techreport
userid: 4893
volume: ~