{"paper":{"title":"Variable selection in functional data classification: a maxima-hunting proposal","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Antonio Cuevas, Jos\\'e L. Torrecilla, Jos\\'e R. Berrendero","submitted_at":"2013-09-26T00:32:50Z","abstract_excerpt":"Variable selection is considered in the setting of supervised binary classification with functional data $\\{X(t),\\ t\\in[0,1]\\}$. By \"variable selection\" we mean any dimension-reduction method which leads to replace the whole trajectory\n  $\\{X(t),\\ t\\in[0,1]\\}$, with a low-dimensional vector $(X(t_1),\\ldots,X(t_k))$ still keeping a similar classification error. Our proposal for variable selection is based on the idea of selecting the local maxima $(t_1,\\ldots,t_k)$ of the function ${\\mathcal V}_X^2(t)={\\mathcal V}^2(X(t),Y)$, where ${\\mathcal V}$ denotes the \"distance covariance\" association me"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1309.6697","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}