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Feature classification and regression tasks for high dimensional data have been handled by many well-known algorithms like feed-forward multi-level perceptron, decision tree, support vector machine, and many others. Recently, a new approach called subspace learning machine (SLM) has been found which finds a balance between simplicity and effectiveness by partitioning an input feature space into multiple discriminant subspaces in a hierarchical manner. The technique has been extended in many directions to handle high dimensional data. We will emphasize the significance of these developments and present experimental results.
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Vinod K. Mishra, C.-C. Jay Kuo, "New approaches to classification and regression for very high dimensional data," Proc. SPIE 12542, Disruptive Technologies in Information Sciences VII, 125420G (15 June 2023); https://doi.org/10.1117/12.2663378