{"id":"https://openalex.org/W1983893422","doi":"https://doi.org/10.1109/iccvw.2009.5457715","title":"Learning invariances with Stationary Subspace Analysis","display_name":"Learning invariances with Stationary Subspace Analysis","publication_year":2009,"publication_date":"2009-09-01","ids":{"openalex":"https://openalex.org/W1983893422","doi":"https://doi.org/10.1109/iccvw.2009.5457715","mag":"1983893422"},"language":"en","primary_location":{"id":"doi:10.1109/iccvw.2009.5457715","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw.2009.5457715","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111490654","display_name":"Frank C. Meinecke","orcid":null},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Frank C. Meinecke","raw_affiliation_strings":["Machine Learning Group, Dept. Computer Science, TU Berlin, Franklinstr. 28/29, FR 6-9, 10587, Germany"],"affiliations":[{"raw_affiliation_string":"Machine Learning Group, Dept. Computer Science, TU Berlin, Franklinstr. 28/29, FR 6-9, 10587, Germany","institution_ids":["https://openalex.org/I4577782"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072294543","display_name":"Paul von B\u00fcnau","orcid":"https://orcid.org/0000-0002-6806-0718"},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Paul von Bunau","raw_affiliation_strings":["Machine Learning Group, Dept. Computer Science, TU Berlin, Franklinstr. 28/29, FR 6-9, 10587, Germany"],"affiliations":[{"raw_affiliation_string":"Machine Learning Group, Dept. Computer Science, TU Berlin, Franklinstr. 28/29, FR 6-9, 10587, Germany","institution_ids":["https://openalex.org/I4577782"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004328317","display_name":"Motoaki Kawanabe","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Motoaki Kawanabe","raw_affiliation_strings":["Intelligent Data Analysis Group, Fraunhofer FIRST.IDA, Kekul\u00e9str. 7, 12489 Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Intelligent Data Analysis Group, Fraunhofer FIRST.IDA, Kekul\u00e9str. 7, 12489 Berlin, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107841529","display_name":"Klaus-R. M\u00fcller","orcid":null},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Klaus-R. Muller","raw_affiliation_strings":["Machine Learning Group, Dept. Computer Science, TU Berlin, Franklinstr. 28/29, FR 6-9, 10587, Germany"],"affiliations":[{"raw_affiliation_string":"Machine Learning Group, Dept. Computer Science, TU Berlin, Franklinstr. 28/29, FR 6-9, 10587, Germany","institution_ids":["https://openalex.org/I4577782"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5111490654"],"corresponding_institution_ids":["https://openalex.org/I4577782"],"apc_list":null,"apc_paid":null,"fwci":2.1148,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.86145312,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"5","issue":null,"first_page":"87","last_page":"92"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.9207735657691956},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.7030805945396423},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5399887561798096},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5214430689811707},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.491120308637619},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.48645374178886414},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.48213693499565125},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.47701096534729004},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4726918041706085},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4446153938770294},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.44050803780555725},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.41929471492767334},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3426941931247711},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.33861643075942993},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.08449390530586243}],"concepts":[{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.9207735657691956},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.7030805945396423},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5399887561798096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5214430689811707},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.491120308637619},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.48645374178886414},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.48213693499565125},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.47701096534729004},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4726918041706085},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4446153938770294},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.44050803780555725},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.41929471492767334},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3426941931247711},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.33861643075942993},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.08449390530586243},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/iccvw.2009.5457715","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw.2009.5457715","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops","raw_type":"proceedings-article"},{"id":"pmh:oai:fraunhofer.de:N-188240","is_oa":false,"landing_page_url":"http://publica.fraunhofer.de/documents/N-188240.html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400801","display_name":"Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Fraunhofer FIRST","raw_type":"Conference Paper"},{"id":"pmh:oai:publica.fraunhofer.de:publica/365708","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/365708","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1521561168","https://openalex.org/W2032558547","https://openalex.org/W2034368206","https://openalex.org/W2053037153","https://openalex.org/W2110603299","https://openalex.org/W2139122730","https://openalex.org/W2141840626","https://openalex.org/W2142183266","https://openalex.org/W2142975003","https://openalex.org/W2162651021","https://openalex.org/W4205778870","https://openalex.org/W6681158929","https://openalex.org/W6681328277"],"related_works":["https://openalex.org/W2896134808","https://openalex.org/W3172436493","https://openalex.org/W4287164812","https://openalex.org/W2957492749","https://openalex.org/W1887135636","https://openalex.org/W2386063599","https://openalex.org/W1975884855","https://openalex.org/W3213150849","https://openalex.org/W4285605394","https://openalex.org/W2025894073"],"abstract_inverted_index":{"Recently,":[0],"a":[1,22,26,78],"novel":[2],"subspace":[3,29],"decomposition":[4],"method,":[5],"termed":[6],"`Stationary":[7],"Subspace":[8],"Analysis'":[9],"(SSA),":[10],"has":[11],"been":[12],"proposed":[13],"by":[14,49],"Bu\u00bfnau":[15],"et":[16],"al..":[17],"SSA":[18,100],"aims":[19],"to":[20,25],"find":[21],"linear":[23],"projection":[24],"lower":[27,79],"dimensional":[28],"such":[30],"that":[31,48,63,86,99],"the":[32,35,51,55,72,82],"distribution":[33],"of":[34,74,84],"projected":[36],"data":[37],"does":[38],"not":[39],"change":[40],"over":[41],"successive":[42],"epochs":[43,85],"or":[44],"sub-datasets.":[45],"We":[46,70],"show":[47,91],"modifying":[50],"loss":[52],"function":[53],"and":[54,67,76],"optimization":[56],"procedure":[57],"we":[58,90],"can":[59,101],"obtain":[60],"an":[61,93],"algorithm":[62],"is":[64,87],"both":[65],"faster":[66],"more":[68],"accurate.":[69],"discuss":[71],"problem":[73],"indeterminacies":[75],"provide":[77],"bound":[80],"on":[81],"number":[83],"needed.":[88],"Finally,":[89],"in":[92,105],"experiment":[94],"with":[95],"simulated":[96],"image":[97],"patches,":[98],"be":[102],"used":[103],"favourably":[104],"invariance":[106],"learning.":[107]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
