Experimenters today frequently quantify millions or even billions of characteristics (measurements) each sample to address critical biological issues, in the hopes that machine learning tools would be able to make correct data-driven judgments. An efficient analysis requires a low-dimensional representation that preserves the differentiating features in data whose size and complexity are orders of magnitude apart (e.g., if a certain ailment is present in the person's body). While there are several systems that can handle millions of variables and yet have strong empirical and conceptual guarantees, there are few that can be clearly understood. This research presents an evaluation of supervised dimensionality reduction for large scale data. We provide a methodology for expanding Principal Component Analysis (PCA) by including category moment estimations in low-dimensional projections. Linear Optimum Low-Rank (LOLR) projection, the cheapest variant, includes the class-conditional means. We show that LOLR projections and its extensions enhance representations of data for future classifications while retaining computing flexibility and reliability using both experimental and simulated data benchmark. When it comes to accuracy, LOLR prediction outperforms other modular linear dimension reduction methods that require much longer computation times on conventional computers. LOLR uses more than 150 million attributes in brain image processing datasets, and many genome sequencing datasets have more than half a million attributes.
Linear Optimum Low-Rank (LOLR), Linear Discriminant Analysis (LDA), Canonical Correlations Analyses (CCA), Principal Component Analysis (PCA), Partial Least Squares (PLS).
Author(s) thanks to Norwegian University of Science and Technology for research lab and equipment support.
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Nancy Jan Sliper, “An Evaluation of Supervised Dimensionality Reduction For Large Scale Data”, Journal of Machine and Computing, vol.2, no.1, pp. 017-025, January 2022. doi: 10.53759/7669/jmc202202003.
© 2022 Nancy Jan Sliper. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
