A simple method for visualizing labelled and unlabelled data in high-dimensional spaces

 

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dc.contributor.author Greene, J R
dc.date.accessioned 2017-04-26T08:08:35Z
dc.date.available 2017-04-26T08:08:35Z
dc.date.issued 2004
dc.identifier.citation Greene, JR. (2004). A simple method for visualizing labelled and unlabelled data in high-dimensional spaces, 45-49
dc.identifier.uri http://hdl.handle.net/11427/24219
dc.description.abstract The low-dimensional visualisation of highdimensional data is a valuable way of detecting structure (such as clusters, and the presence of outliers) in the data, and avoiding some of the pitfalls of blind data manipulation. Projection based on principal component analysis is widely employed and often useful, but it is a variancepreserving projection which takes no account of class labels, and may, for this reason, hide significant structure. Here we present a very simple method which appears to yield useful visualizations for many datasets. It is based on a random search for a linear transformation, and projection into a twodimensional visual space, which maximises an objective measure of class separability in the visual space. The method, which can be thought of as a variant of projection pursuit with a novel interest measure, is demonstrated on datasets from the UCI Repository. Tentative interim results are also given for a proposed extension based on spectral clustering, for extending the method to unlabelled data.
dc.relation.ispartof Proceedings of the 15th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Grabouw, South Africa
dc.title A simple method for visualizing labelled and unlabelled data in high-dimensional spaces
dc.date.updated 2016-01-07T08:33:27Z
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Engineering & the Built Environment en_ZA
dc.publisher.department Department of Electrical Engineering en_ZA
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