{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:28:07Z","timestamp":1758266887388},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"01","license":[{"start":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T00:00:00Z","timestamp":1563321600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.aaai.org"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>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 \u201cbest first\u201d 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.<\/jats:p>","DOI":"10.1609\/aaai.v33i01.33012280","type":"journal-article","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T07:41:03Z","timestamp":1568101263000},"page":"2280-2287","source":"Crossref","is-referenced-by-count":12,"title":["Heuristic Search Algorithm for Dimensionality Reduction Optimally Combining Feature Selection and Feature Extraction"],"prefix":"10.1609","volume":"33","author":[{"given":"Baokun","family":"He","sequence":"first","affiliation":[]},{"given":"Swair","family":"Shah","sequence":"additional","affiliation":[]},{"given":"Crystal","family":"Maung","sequence":"additional","affiliation":[]},{"given":"Gordon","family":"Arnold","sequence":"additional","affiliation":[]},{"given":"Guihong","family":"Wan","sequence":"additional","affiliation":[]},{"given":"Haim","family":"Schweitzer","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2019,7,17]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/4066\/3944","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/4066\/3944","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T07:09:02Z","timestamp":1667804942000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/4066"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,17]]},"references-count":0,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2019,7,23]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v33i01.33012280","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2019,7,17]]}}}