Heuristic Search Algorithm for Dimensionality Reduction Optimally Combining Feature Selection and Feature Extraction

Citation:

Baokun He, Swair Shah, Crystal Maung, Gordon Arnold, Guihong Wan, and Haim Schwitzer. 2019. “Heuristic Search Algorithm for Dimensionality Reduction Optimally Combining Feature Selection and Feature Extraction.” Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-19). Publisher's Version

Abstract:

The following are two classical approaches to dimensionality reduction: 1. Approximating the data with a small number of features that exist in the data (feature selection). 2. Approximating the data with a small number of arbitrary features (feature extraction). We study a generalization that approximates the data with both selected and extracted features. We show that an optimal solution to this hybrid problem involves a combinatorial search, and cannot be trivially obtained even if one can solve optimally the separate problems of selection and extraction. Our approach that gives optimal and approximate solutions uses a “best first” heuristic search. The algorithm comes with both an a priori and an a posteriori optimality guarantee similar to those that can be obtained for the classical weighted A* algorithm. Experimental results show the effectiveness of the proposed approach.
Last updated on 12/17/2021