Get quick answers to your questions about the article from our AI researcher chatbot
{'id': 'https://openalex.org/W4226139248', 'doi': 'https://doi.org/10.1109/tnnls.2020.3027351', 'title': 'Robust Matrix Factorization With Spectral Embedding', 'display_name': 'Robust Matrix Factorization With Spectral Embedding', 'publication_year': 2020, 'publication_date': '2020-10-22', 'ids': {'openalex': 'https://openalex.org/W4226139248', 'doi': 'https://doi.org/10.1109/tnnls.2020.3027351', 'pmid': 'https://pubmed.ncbi.nlm.nih.gov/33090957'}, 'language': 'en', 'primary_location': {'is_oa': False, 'landing_page_url': 'https://doi.org/10.1109/tnnls.2020.3027351', 'pdf_url': None, 'source': {'id': 'https://openalex.org/S4210175523', 'display_name': 'IEEE Transactions on Neural Networks and Learning Systems', 'issn_l': '2162-237X', 'issn': ['2162-237X', '2162-2388'], 'is_oa': False, 'is_in_doaj': False, 'is_core': True, 'host_organization': 'https://openalex.org/P4310319808', 'host_organization_name': 'Institute of Electrical and Electronics Engineers', 'host_organization_lineage': ['https://openalex.org/P4310319808'], 'host_organization_lineage_names': ['Institute of Electrical and Electronics Engineers'], 'type': 'journal'}, 'license': None, 'license_id': None, 'version': None, 'is_accepted': False, 'is_published': False}, 'type': 'article', 'type_crossref': 'journal-article', 'indexed_in': ['crossref', 'pubmed'], 'open_access': {'is_oa': False, 'oa_status': 'closed', 'oa_url': None, 'any_repository_has_fulltext': False}, 'authorships': [{'author_position': 'first', 'author': {'id': 'https://openalex.org/A5087975803', 'display_name': 'Mulin Chen', 'orcid': 'https://orcid.org/0000-0003-4634-3802'}, 'institutions': [{'id': 'https://openalex.org/I17145004', 'display_name': 'Northwestern Polytechnical University', 'ror': 'https://ror.org/01y0j0j86', 'country_code': 'CN', 'type': 'education', 'lineage': ['https://openalex.org/I17145004']}], 'countries': ['CN'], 'is_corresponding': False, 'raw_author_name': 'Mulin Chen', 'raw_affiliation_strings': ["Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, China"], 'affiliations': [{'raw_affiliation_string': "Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, China", 'institution_ids': ['https://openalex.org/I17145004']}]}, {'author_position': 'last', 'author': {'id': 'https://openalex.org/A5106943753', 'display_name': 'Xuelong Li', 'orcid': 'https://orcid.org/0000-0003-2924-946X'}, 'institutions': [{'id': 'https://openalex.org/I17145004', 'display_name': 'Northwestern Polytechnical University', 'ror': 'https://ror.org/01y0j0j86', 'country_code': 'CN', 'type': 'education', 'lineage': ['https://openalex.org/I17145004']}], 'countries': ['CN'], 'is_corresponding': False, 'raw_author_name': 'Xuelong Li', 'raw_affiliation_strings': ["Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, China"], 'affiliations': [{'raw_affiliation_string': "Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, China", 'institution_ids': ['https://openalex.org/I17145004']}]}], 'institution_assertions': [], 'countries_distinct_count': 1, 'institutions_distinct_count': 1, 'corresponding_author_ids': [], 'corresponding_institution_ids': [], 'apc_list': None, 'apc_paid': None, 'fwci': 1.631, 'has_fulltext': False, 'cited_by_count': 6, 'citation_normalized_percentile': {'value': 0.770853, 'is_in_top_1_percent': False, 'is_in_top_10_percent': False}, 'cited_by_percentile_year': {'min': 81, 'max': 82}, 'biblio': {'volume': '32', 'issue': '12', 'first_page': '5698', 'last_page': '5707'}, 'is_retracted': False, 'is_paratext': False, 'primary_topic': {'id': 'https://openalex.org/T10057', 'display_name': 'Face and Expression Recognition', 'score': 0.9954, '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'}}, 'topics': [{'id': 'https://openalex.org/T10057', 'display_name': 'Face and Expression Recognition', 'score': 0.9954, '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'}}, {'id': 'https://openalex.org/T10689', 'display_name': 'Remote-Sensing Image Classification', 'score': 0.9936, 'subfield': {'id': 'https://openalex.org/subfields/2214', 'display_name': 'Media Technology'}, '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/T13731', 'display_name': 'Advanced Computing and Algorithms', 'score': 0.9747, 'subfield': {'id': 'https://openalex.org/subfields/3322', 'display_name': 'Urban Studies'}, 'field': {'id': 'https://openalex.org/fields/33', 'display_name': 'Social Sciences'}, 'domain': {'id': 'https://openalex.org/domains/2', 'display_name': 'Social Sciences'}}], 'keywords': [{'id': 'https://openalex.org/keywords/non-negative-matrix-factorization', 'display_name': 'Non-negative Matrix Factorization', 'score': 0.8248489}, {'id': 'https://openalex.org/keywords/spectral-clustering', 'display_name': 'Spectral Clustering', 'score': 0.73367906}, {'id': 'https://openalex.org/keywords/matrix-norm', 'display_name': 'Matrix norm', 'score': 0.61018103}, {'id': 'https://openalex.org/keywords/matrix', 'display_name': 'Matrix (chemical analysis)', 'score': 0.4137045}], 'concepts': [{'id': 'https://openalex.org/C152671427', 'wikidata': 'https://www.wikidata.org/wiki/Q10843505', 'display_name': 'Non-negative matrix factorization', 'level': 4, 'score': 0.8248489}, {'id': 'https://openalex.org/C73555534', 'wikidata': 'https://www.wikidata.org/wiki/Q622825', 'display_name': 'Cluster analysis', 'level': 2, 'score': 0.79742974}, {'id': 'https://openalex.org/C42355184', 'wikidata': 'https://www.wikidata.org/wiki/Q1361088', 'display_name': 'Matrix decomposition', 'level': 3, 'score': 0.7553444}, {'id': 'https://openalex.org/C105611402', 'wikidata': 'https://www.wikidata.org/wiki/Q2976589', 'display_name': 'Spectral clustering', 'level': 3, 'score': 0.73367906}, {'id': 'https://openalex.org/C79337645', 'wikidata': 'https://www.wikidata.org/wiki/Q779824', 'display_name': 'Outlier', 'level': 2, 'score': 0.6106179}, {'id': 'https://openalex.org/C92207270', 'wikidata': 'https://www.wikidata.org/wiki/Q939253', 'display_name': 'Matrix norm', 'level': 3, 'score': 0.61018103}, {'id': 'https://openalex.org/C41608201', 'wikidata': 'https://www.wikidata.org/wiki/Q980509', 'display_name': 'Embedding', 'level': 2, 'score': 0.53193295}, {'id': 'https://openalex.org/C41008148', 'wikidata': 'https://www.wikidata.org/wiki/Q21198', 'display_name': 'Computer science', 'level': 0, 'score': 0.49946308}, {'id': 'https://openalex.org/C153180895', 'wikidata': 'https://www.wikidata.org/wiki/Q7148389', 'display_name': 'Pattern recognition (psychology)', 'level': 2, 'score': 0.47412467}, {'id': 'https://openalex.org/C191795146', 'wikidata': 'https://www.wikidata.org/wiki/Q3878446', 'display_name': 'Norm (philosophy)', 'level': 2, 'score': 0.421091}, {'id': 'https://openalex.org/C33923547', 'wikidata': 'https://www.wikidata.org/wiki/Q395', 'display_name': 'Mathematics', 'level': 0, 'score': 0.4171843}, {'id': 'https://openalex.org/C154945302', 'wikidata': 'https://www.wikidata.org/wiki/Q11660', 'display_name': 'Artificial intelligence', 'level': 1, 'score': 0.41671878}, {'id': 'https://openalex.org/C106487976', 'wikidata': 'https://www.wikidata.org/wiki/Q685816', 'display_name': 'Matrix (chemical analysis)', 'level': 2, 'score': 0.4137045}, {'id': 'https://openalex.org/C11413529', 'wikidata': 'https://www.wikidata.org/wiki/Q8366', 'display_name': 'Algorithm', 'level': 1, 'score': 0.3815843}, {'id': 'https://openalex.org/C158693339', 'wikidata': 'https://www.wikidata.org/wiki/Q190524', 'display_name': 'Eigenvalues and eigenvectors', 'level': 2, 'score': 0.0}, {'id': 'https://openalex.org/C121332964', 'wikidata': 'https://www.wikidata.org/wiki/Q413', 'display_name': 'Physics', 'level': 0, 'score': 0.0}, {'id': 'https://openalex.org/C192562407', 'wikidata': 'https://www.wikidata.org/wiki/Q228736', 'display_name': 'Materials science', 'level': 0, 'score': 0.0}, {'id': 'https://openalex.org/C159985019', 'wikidata': 'https://www.wikidata.org/wiki/Q181790', 'display_name': 'Composite material', 'level': 1, 'score': 0.0}, {'id': 'https://openalex.org/C62520636', 'wikidata': 'https://www.wikidata.org/wiki/Q944', 'display_name': 'Quantum mechanics', 'level': 1, 'score': 0.0}, {'id': 'https://openalex.org/C17744445', 'wikidata': 'https://www.wikidata.org/wiki/Q36442', 'display_name': 'Political science', 'level': 0, 'score': 0.0}, {'id': 'https://openalex.org/C199539241', 'wikidata': 'https://www.wikidata.org/wiki/Q7748', 'display_name': 'Law', 'level': 1, 'score': 0.0}], 'mesh': [], 'locations_count': 2, 'locations': [{'is_oa': False, 'landing_page_url': 'https://doi.org/10.1109/tnnls.2020.3027351', 'pdf_url': None, 'source': {'id': 'https://openalex.org/S4210175523', 'display_name': 'IEEE Transactions on Neural Networks and Learning Systems', 'issn_l': '2162-237X', 'issn': ['2162-237X', '2162-2388'], 'is_oa': False, 'is_in_doaj': False, 'is_core': True, 'host_organization': 'https://openalex.org/P4310319808', 'host_organization_name': 'Institute of Electrical and Electronics Engineers', 'host_organization_lineage': ['https://openalex.org/P4310319808'], 'host_organization_lineage_names': ['Institute of Electrical and Electronics Engineers'], 'type': 'journal'}, 'license': None, 'license_id': None, 'version': None, 'is_accepted': False, 'is_published': False}, {'is_oa': False, 'landing_page_url': 'https://pubmed.ncbi.nlm.nih.gov/33090957', 'pdf_url': None, 'source': {'id': 'https://openalex.org/S4306525036', 'display_name': 'PubMed', 'issn_l': None, 'issn': None, 'is_oa': False, 'is_in_doaj': False, 'is_core': False, 'host_organization': 'https://openalex.org/I1299303238', 'host_organization_name': 'National Institutes of Health', 'host_organization_lineage': ['https://openalex.org/I1299303238'], 'host_organization_lineage_names': ['National Institutes of Health'], 'type': 'repository'}, 'license': None, 'license_id': None, 'version': None, 'is_accepted': False, 'is_published': False}], 'best_oa_location': None, 'sustainable_development_goals': [], 'grants': [{'funder': 'https://openalex.org/F4320321001', 'funder_display_name': 'National Natural Science Foundation of China', 'award_id': '61871470'}, {'funder': 'https://openalex.org/F4320335777', 'funder_display_name': 'National Key Research and Development Program of China', 'award_id': '2018YFB1107400'}], 'datasets': [], 'versions': [], 'referenced_works_count': 58, 'referenced_works': ['https://openalex.org/W1504886279', 'https://openalex.org/W1510147702', 'https://openalex.org/W1906374873', 'https://openalex.org/W1907775068', 'https://openalex.org/W2007972815', 'https://openalex.org/W2055608878', 'https://openalex.org/W2077583079', 'https://openalex.org/W2096044434', 'https://openalex.org/W2101324110', 'https://openalex.org/W2103560185', 'https://openalex.org/W2108119513', 'https://openalex.org/W2112796928', 'https://openalex.org/W2117553576', 'https://openalex.org/W2121647436', 'https://openalex.org/W2121947440', 'https://openalex.org/W2122504063', 'https://openalex.org/W2123921160', 'https://openalex.org/W2127218421', 'https://openalex.org/W2131828344', 'https://openalex.org/W2134529554', 'https://openalex.org/W2135029798', 'https://openalex.org/W2147278762', 'https://openalex.org/W2152322845', 'https://openalex.org/W2154415691', 'https://openalex.org/W2155754954', 'https://openalex.org/W2162316550', 'https://openalex.org/W2164635368', 'https://openalex.org/W2165874743', 'https://openalex.org/W2166049352', 'https://openalex.org/W2171837816', 'https://openalex.org/W2329664619', 'https://openalex.org/W2407348013', 'https://openalex.org/W2422268042', 'https://openalex.org/W2494395359', 'https://openalex.org/W2560185252', 'https://openalex.org/W2571268788', 'https://openalex.org/W2573763768', 'https://openalex.org/W2577472518', 'https://openalex.org/W2579597427', 'https://openalex.org/W2604632143', 'https://openalex.org/W2605100742', 'https://openalex.org/W2607323999', 'https://openalex.org/W2740464254', 'https://openalex.org/W2740503509', 'https://openalex.org/W2741236095', 'https://openalex.org/W2752190933', 'https://openalex.org/W2758611985', 'https://openalex.org/W2768166594', 'https://openalex.org/W2801528614', 'https://openalex.org/W2809506935', 'https://openalex.org/W2902688686', 'https://openalex.org/W2911671827', 'https://openalex.org/W2912580424', 'https://openalex.org/W2914429466', 'https://openalex.org/W2951019399', 'https://openalex.org/W2954752948', 'https://openalex.org/W2963089385', 'https://openalex.org/W2972477132'], 'related_works': ['https://openalex.org/W4390394189', 'https://openalex.org/W34555840', 'https://openalex.org/W2972997031', 'https://openalex.org/W2792706544', 'https://openalex.org/W2539013788', 'https://openalex.org/W2156699640', 'https://openalex.org/W2127243424', 'https://openalex.org/W2045265907', 'https://openalex.org/W2037504162', 'https://openalex.org/W1568451138'], 'abstract_inverted_index': {'Nonnegative': [0], 'matrix': [1, 101], 'factorization': [2, 39], '(NMF)': [3], 'and': [4, 24, 54, 79, 100, 114, 144, 173], 'spectral': [5, 25, 55, 98], 'clustering': [6, 14, 26, 99, 177], 'are': [7, 85, 93, 106, 129, 142, 149], 'two': [8], 'of': [9, 52, 76, 82, 90, 122, 139, 170, 182], 'the': [10, 21, 29, 50, 65, 73, 80, 103, 110, 116, 124, 127, 132, 137, 140, 146, 154, 168, 180, 183], 'most': [11], 'widely': [12], 'used': [13], 'techniques.': [15], 'However,': [16], 'NMF': [17, 53], 'cannot': [18], 'deal': [19], 'with': [20, 40, 131], 'nonlinear': [22, 111], 'data,': [23], 'relies': [27], 'on': [28, 162], 'postprocessing.': [30], 'In': [31, 61], 'this': [32, 91], 'article,': [33], 'we': [34, 71], 'propose': [35], 'a': [36], 'Robust': [37], 'Matrix': [38], 'Spectral': [41], 'embedding': [42], '(RMS)': [43], 'approach': [44], 'for': [45, 156], 'data': [46, 66, 112, 165], 'clustering,': [47, 56], 'which': [48, 152], 'inherits': [49], 'advantages': [51], 'while': [57], 'avoiding': [58], 'their': [59, 175], 'shortcomings.': [60], 'addition,': [62], 'to': [63, 108], 'cluster': [64, 117], 'represented': [67], 'by': [68, 96], 'multiple': [69], 'views,': [70], 'present': [72], 'multiview': [74], 'version': [75], 'RMS': [77], '(M-RMS),': [78], 'weights': [81], 'different': [83], 'views': [84], 'self-tuned.': [86], 'The': [87], 'main': [88], 'contributions': [89], 'research': [92], 'threefold:': [94], '1)': [95], 'integrating': [97], 'factorization,': [102], 'proposed': [104, 147], 'methods': [105, 148, 172], 'able': [107], 'capture': [109], 'structure': [113], 'obtain': [115], 'indicator': [118], 'directly;': [119], '2)': [120], 'instead': [121], 'using': [123], 'squared': [125], 'Frobenius-norm,': [126], 'objectives': [128], 'developed': [130], 'l2,1': [133], '-norm,': [134], 'such': [135], 'that': [136], 'effects': [138], 'outliers': [141], 'alleviated;': [143], '3)': [145], 'totally': [150], 'parameter-free,': [151], 'increases': [153], 'applicability': [155], 'various': [157], 'real-world': [158], 'problems.': [159], 'Extensive': [160], 'experiments': [161], 'several': [163], 'single-view/multiview': [164], 'sets': [166], 'demonstrate': [167], 'effectiveness': [169], 'our': [171], 'verify': [174], 'superior': [176], 'performance': [178], 'over': [179], 'state': [181], 'arts.': [184]}, 'cited_by_api_url': 'https://api.openalex.org/works?filter=cites:W4226139248', 'counts_by_year': [{'year': 2024, 'cited_by_count': 1}, {'year': 2023, 'cited_by_count': 2}, {'year': 2021, 'cited_by_count': 3}], 'updated_date': '2024-12-11T00:23:02.340883', 'created_date': '2022-05-05'}