Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models
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| Authors | Makoto Aoshima, Kazuyoshi Yata |
| Journal/Conference Name | Annals of the Institute of Statistical Mathematics |
| Paper Category | Artificial Intelligence |
| Paper Abstract | We consider classifiers for high-dimensional data under the strongly spiked eigenvalue (SSE) model. We first show that high-dimensional data often have the SSE model. We consider a distance-based classifier using eigenstructures for the SSE model. We apply the noise reduction methodology to estimation of the eigenvalues and eigenvectors in the SSE model. We create a new distance-based classifier by transforming data from the SSE model to the non-SSE model. We give simulation studies and discuss the performance of the new classifier. Finally, we demonstrate the new classifier by using microarray data sets. |
| Date of publication | 2017 |
| Code Programming Language | Jupyter Notebook |
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