Robust automatic data decomposition using a modified sparse NMF

Samko, Oksana, Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884 and Marshall, Andrew David ORCID: https://orcid.org/0000-0003-2789-1395 2007. Robust automatic data decomposition using a modified sparse NMF. Presented at: MIRAGE 2007, Rocquencourt, France, 28-30 March 2007. Computer Vision/Computer Graphics Collaboration Techniques. Lecture Notes in Computer Science (4418/2) Berlin / Heidelberg: Springer, pp. 225-234. 10.1007/978-3-540-71457-6_21

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Abstract

In this paper, we address the problem of automating the partial representation from real world data with an unknown a priori structure. Such representation could be very useful for the further construction of an automatic hierarchical data model. We propose a three stage process using data normalisation and the data intrinsic dimensionality estimation as the first step. The second stage uses a modified sparse Non-negative matrix factorization (sparse NMF) algorithm to perform the initial segmentation. At the final stage region growing algorithm is applied to construct a mask of the original data. Our algorithm has a very broad range of a potential applications, we illustrate this versatility by applying the algorithm to several dissimilar data sets.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Additional Information:	Third International Conference, MIRAGE 2007, Rocquencourt, France, March 28-30, 2007. Proceedings
Publisher:	Springer
ISBN:	9783540714569
Related URLs:	Publisher
Last Modified:	17 Oct 2022 09:42
URI:	https://orca.cardiff.ac.uk/id/eprint/5328

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