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Robust automatic data decomposition using a modified sparse NMF

Samko, Oksana, Rosin, Paul L. ORCID: and Marshall, Andrew David ORCID: 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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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
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Last Modified: 17 Oct 2022 09:42

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