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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Runkler, T.A. Bezdek, J.C. |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Electr. & Electron. Eng., Univ. of Melbourne, Melbourne, VIC, Australia (Bezdek, J.C.) || Corp. Technol., Siemens AG, Munich, Germany (Runkler, T.A.) |
| Abstract | We introduce a new method for multidimensional scaling in dissimilarity data that is based on preservation of metric topology between the original and derived data sets. The model seeks neighbors in the derived data that have the same ranks as in the input data. The algorithm we use to optimize the model is a modification of particle swarm optimization called multiswarming. We compare the new method to three well known approaches: Principal component analysis, Sammon's method, and (Kruskal's) metric MDS. Our method produces feature vector realizations that compare favorably with the other approaches on three real relational data sets. |
| Starting Page | 2940 |
| Ending Page | 2946 |
| File Size | 1969846 |
| Page Count | 7 |
| File Format | |
| e-ISBN | 9781479914883 |
| DOI | 10.1109/CEC.2014.6900225 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-07-06 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Vectors Measurement Image color analysis Eigenvalues and eigenfunctions Linear programming Principal component analysis Optimization Sammon's algorithm metric topology preservation multidimensional scaling multiswarm optimization |
| Content Type | Text |
| Resource Type | Article |
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