{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/86771"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/86771","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"A Study on Multi-Output Multi-Kernel Recursive Least Squares Against Outliers","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Bhattacharyya, Ahona"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Slavakis, Konstantinos","Electrical Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-21T21:44:55Z","date_published":"2025-02-21T21:44:55Z","updated_at":"2026-07-27T19:05:37Z","subjects":["electrical engineering"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/86771","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Slavakis, Konstantinos","Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Bhattacharyya, Ahona"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-02-21T21:44:55Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["electrical engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/86771"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","The hierarchical-optimization recursive least squares (HO-RLS) was studied in the context of non-linear, multi-output, non-parametric and robust online regression in presence of sparse outliers. Outliers are modeled as nuisance variables that are evaluated jointly with the latent non-linear system via a sparsity inducing (non-)convex regularized least-squares task. The suggested outlier robust HO-RLS operates under the assumption that the underlying non-linear system allows a multi-kernel expansion. It is built on steepest descent directions with a constant step size (learning rate), has set bound on its computational complexity, requires no matrix inversion (lemma) and accommodates coloured nominal noise of known correlation matrix. Numerical data(synthetically generated and real) testing is done for non-stationary scenarios to show the notable improvements over state-of-the-art techniques.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A Study on Multi-Output Multi-Kernel Recursive Least Squares Against Outliers"]}]}],"canonical_facts":{"dc:contributor":["Slavakis, Konstantinos","Electrical Engineering"],"dc:creator":["Bhattacharyya, Ahona"],"dc:date":["2025-02-21T21:44:55Z","2020"],"dc:description":["M.S.","The hierarchical-optimization recursive least squares (HO-RLS) was studied in the context of non-linear, multi-output, non-parametric and robust online regression in presence of sparse outliers. Outliers are modeled as nuisance variables that are evaluated jointly with the latent non-linear system via a sparsity inducing (non-)convex regularized least-squares task. The suggested outlier robust HO-RLS operates under the assumption that the underlying non-linear system allows a multi-kernel expansion. It is built on steepest descent directions with a constant step size (learning rate), has set bound on its computational complexity, requires no matrix inversion (lemma) and accommodates coloured nominal noise of known correlation matrix. Numerical data(synthetically generated and real) testing is done for non-stationary scenarios to show the notable improvements over state-of-the-art techniques.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/86771"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["electrical engineering"],"dc:title":["A Study on Multi-Output Multi-Kernel Recursive Least Squares Against Outliers"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:37Z"}