Visualization of high-dimensional data has always been a challenging task. Here we discuss and propose variants of non-linear data projection methods (Generative Topographic Mapping (GTM) and GTM with simultaneous feature saliency (GTM-FS)) that are adapted to be effective on very high-dimensional data. The adaptations use log space values at certain steps of the Expectation Maximization (EM) algorithm and during the visualization process. We have tested the proposed algorithms by visualizing electrostatic potential data for Major Histocompatibility Complex (MHC) class-I proteins. The experiments show that the variation in the original version of GTM and GTM-FS worked successfully with data of more than 2000 dimensions and we compare the results with other linear/nonlinear projection methods: Principal Component Analysis (PCA), Neuroscale (NSC) and Gaussian Process Latent Variable Model (GPLVM).
|Title of host publication||2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)|
|Number of pages||8|
|Publication status||Published - 2012|
|Event||2012 IEEE symposium on computational intelligence in bioinformatics and computational biology - San Diego, California, United States|
Duration: 9 May 2012 → 12 May 2012
|Conference||2012 IEEE symposium on computational intelligence in bioinformatics and computational biology|
|City||San Diego, California|
|Period||9/05/12 → 12/05/12|
|Other||This symposium will bring together top researchers, practitioners, and students from around the world to discuss the latest advances in the field of Computational Intelligence and its application to real world problems in biology, bioinformatics, computational biology, chemical informatics, bioengineering and related fields. Computational Intelligence (CI) approaches include artificial neural networks and machine learning techniques, fuzzy logic, evolutionary algorithms and meta-heuristics, hybrid approaches and other emerging techniques.|
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Mumtaz, S., Nabney, I., & Flower, D. (2012). Novel visualization methods for protein data. In 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp. 198-205). IEEE.