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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Samko, O., Rosin, P. L., & Marshall, A. D. (2007). Robust automatic data decomposition using a modified sparse NMF. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4418 LNCS, pp. 225–234). Springer Verlag. https://doi.org/10.1007/978-3-540-71457-6_21
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