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- Publisher Website: 10.1016/j.jbi.2005.02.002
- Scopus: eid_2-s2.0-23244445943
- PMID: 16084474
- WOS: WOS:000231514200010
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Article: Deterministic projection by growing cell structure networks for visualization of high-dimensionality datasets
Title | Deterministic projection by growing cell structure networks for visualization of high-dimensionality datasets |
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Authors | |
Keywords | Data visualization Topology preserving maps Self-organizing maps Random projection High-dimensionality data Growing cell structure networks Feature transformation Clinical proteomics dataset |
Issue Date | 2005 |
Citation | Journal of Biomedical Informatics, 2005, v. 38, n. 4, p. 322-330 How to Cite? |
Abstract | Recent advances in clinical proteomics data acquisition have led to the generation of datasets of high complexity and dimensionality. We present here a visualization method for high-dimensionality datasets that makes use of neuronal vectors of a trained growing cell structure (GCS) network for the projection of data points onto two dimensions. The use of a GCS network enables the generation of the projection matrix deterministically rather than randomly as in random projection. Three datasets were used to benchmark the performance and to demonstrate the use of this deterministic projection approach in real-life scientific applications. Comparisons are made to an existing self-organizing map projection method and random projection. The results suggest that deterministic projection outperforms existing methods and is suitable for the visualization of datasets of very high dimensionality. © 2005 Elsevier Inc. All rights reserved. |
Persistent Identifier | http://hdl.handle.net/10722/250851 |
ISSN | 2023 Impact Factor: 4.0 2023 SCImago Journal Rankings: 1.160 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wong, Jason W.H. | - |
dc.contributor.author | Cartwright, Hugh M. | - |
dc.date.accessioned | 2018-02-01T01:53:54Z | - |
dc.date.available | 2018-02-01T01:53:54Z | - |
dc.date.issued | 2005 | - |
dc.identifier.citation | Journal of Biomedical Informatics, 2005, v. 38, n. 4, p. 322-330 | - |
dc.identifier.issn | 1532-0464 | - |
dc.identifier.uri | http://hdl.handle.net/10722/250851 | - |
dc.description.abstract | Recent advances in clinical proteomics data acquisition have led to the generation of datasets of high complexity and dimensionality. We present here a visualization method for high-dimensionality datasets that makes use of neuronal vectors of a trained growing cell structure (GCS) network for the projection of data points onto two dimensions. The use of a GCS network enables the generation of the projection matrix deterministically rather than randomly as in random projection. Three datasets were used to benchmark the performance and to demonstrate the use of this deterministic projection approach in real-life scientific applications. Comparisons are made to an existing self-organizing map projection method and random projection. The results suggest that deterministic projection outperforms existing methods and is suitable for the visualization of datasets of very high dimensionality. © 2005 Elsevier Inc. All rights reserved. | - |
dc.language | eng | - |
dc.relation.ispartof | Journal of Biomedical Informatics | - |
dc.subject | Data visualization | - |
dc.subject | Topology preserving maps | - |
dc.subject | Self-organizing maps | - |
dc.subject | Random projection | - |
dc.subject | High-dimensionality data | - |
dc.subject | Growing cell structure networks | - |
dc.subject | Feature transformation | - |
dc.subject | Clinical proteomics dataset | - |
dc.title | Deterministic projection by growing cell structure networks for visualization of high-dimensionality datasets | - |
dc.type | Article | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1016/j.jbi.2005.02.002 | - |
dc.identifier.pmid | 16084474 | - |
dc.identifier.scopus | eid_2-s2.0-23244445943 | - |
dc.identifier.volume | 38 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 322 | - |
dc.identifier.epage | 330 | - |
dc.identifier.isi | WOS:000231514200010 | - |
dc.identifier.issnl | 1532-0464 | - |