{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fast-forward-feature-selection-for-the","title":"Fast forward feature selection for the nonlinear classification of hyperspectral images","arxiv_id":"1501.00857","date":"2015-01-05","proceeding":null,"authors":["Mathieu Fauvel","Clement Dechesne","Anthony Zullo","Frédéric Ferraty"],"abstract":"A fast forward feature selection algorithm is presented in this paper. It is\nbased on a Gaussian mixture model (GMM) classifier. GMM are used for\nclassifying hyperspectral images. The algorithm selects iteratively spectral\nfeatures that maximizes an estimation of the classification rate. The\nestimation is done using the k-fold cross validation. In order to perform fast\nin terms of computing time, an efficient implementation is proposed. First, the\nGMM can be updated when the estimation of the classification rate is computed,\nrather than re-estimate the full model. Secondly, using marginalization of the\nGMM, sub models can be directly obtained from the full model learned with all\nthe spectral features. Experimental results for two real hyperspectral data\nsets show that the method performs very well in terms of classification\naccuracy and processing time. Furthermore, the extracted model contains very\nfew spectral channels.","url_abs":"http://arxiv.org/abs/1501.00857v1","url_pdf":"http://arxiv.org/pdf/1501.00857v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fast-forward-feature-selection-for-the","repo_url":"https://github.com/mfauvel/FFFS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"hyperspectral-image-classification","task_name":"Hyperspectral Image Classification"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}