A simple method for visualizing labelled and unlabelled data in high-dimensional spaces
| 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.date.updated | 2016-01-07T08:33:27Z | |
| 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.identifier.apacitation | 2004. <i>A simple method for visualizing labelled and unlabelled data in high-dimensional spaces.</i> http://hdl.handle.net/11427/24219 | en_ZA |
| dc.identifier.chicagocitation | . 2004. <i>A simple method for visualizing labelled and unlabelled data in high-dimensional spaces.</i> http://hdl.handle.net/11427/24219 | en_ZA |
| dc.identifier.citation | Greene, JR. (2004). A simple method for visualizing labelled and unlabelled data in high-dimensional spaces, 45-49 | |
| dc.identifier.ris | TY - AU - Greene, J R AB - 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. DA - 2004 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2004 T1 - A simple method for visualizing labelled and unlabelled data in high-dimensional spaces TI - A simple method for visualizing labelled and unlabelled data in high-dimensional spaces UR - http://hdl.handle.net/11427/24219 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/24219 | |
| dc.identifier.vancouvercitation | . 2004. <i>A simple method for visualizing labelled and unlabelled data in high-dimensional spaces.</i> http://hdl.handle.net/11427/24219 | en_ZA |
| dc.language.iso | eng | |
| dc.publisher.department | Department of Electrical Engineering | en_ZA |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | |
| dc.publisher.institution | University of Cape Town | |
| 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.type | Other | |
| uct.type.filetype | Text | |
| uct.type.filetype | Image |