Feature extraction methods do generally not account for hierarchical structure in the data. For example, PCA and ICA provide transformations that solely depend on global properties of the overall dataset. We here present a general approach for the extraction of feature hierarchies from datasets and their use for classification or clustering. A hierarchy of features extracted from a dataset thereby constitutes a compact representation of the set that on the one hand can be used to characterize and understand the data and on the other hand serves as a basis to classify or cluster a collection of datasets. As a proof of concept, we demonstrate the feasibility of this approach with an application to mixtures of Gaussians with varying degree of structuredness and to a clinical EEG recording. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Schubert, M., & Kohlmorgen, J. (2008). Hierarchical feature extraction for compact representation and classification of datasets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4984 LNCS, pp. 556–565). https://doi.org/10.1007/978-3-540-69158-7_58
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