{"doi":"10.1109/icist59754.2023.10367150","title":"PHASL-NMF: Hierarchical ALS Based Power Non-Negative Matrix Factorization","abstract":null,"journal":"2023 13th International Conference on Information Science and Technology (ICIST)","year":2023,"id":623698,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":320661,"name":"Bing Han","orcid":"0000-0002-7299-2753","position":1,"is_corresponding":false},{"id":390383,"name":"Nian Zhang","orcid":"0000-0003-1916-7719","position":2,"is_corresponding":false},{"id":1116353,"name":"Peng Zhou","orcid":"0000-0001-8619-1124","position":3,"is_corresponding":false},{"id":1187750,"name":"Jiang Xiong","orcid":"0000-0003-0158-1399","position":4,"is_corresponding":false},{"id":1611999,"name":"Yuzhi Zhao","orcid":null,"position":5,"is_corresponding":false},{"id":1612000,"name":"Li Xiu Chen","orcid":null,"position":6,"is_corresponding":false},{"id":1187748,"name":"Xiangguang Dai","orcid":"0000-0003-1846-2580","position":7,"is_corresponding":false},{"id":584467,"name":"Yuan Luo","orcid":"0000-0003-3153-7495","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"PHASL-NMF: Hierarchical ALS Based Power Non-Negative Matrix Factorization","abstract":"The non-negative matrix factorization (NMF) has been found an effective clustering algorithm and it outperforms the classical k-means algorithm. Existing researches mainly focus on the problem of reducing the decomposition error between two decomposition matrices and the original matrix. In this paper, inspired by the power k-means algorithm and the hierarchical alternating least square NMF, we propose a novel NMF algorithm called power NMF (PHALS-NMF), which introduces the power mean to reduce decomposition error. Massive experiments on several datasets show the feasibility and effectiveness of PHALS-NMF.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W4390394189","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.2260066,"influential_citations":0,"citation_trend":[],"oa_status":"closed","license":null,"oa_locations":[{"url":"http://dx.doi.org/10.1109/icist59754.2023.10367150","host_type":""}],"fields_of_study":["Face and Expression Recognition","Image Retrieval and Classification Techniques","Advanced Data Compression Techniques"],"mesh_terms":[],"keywords":["Non-negative matrix factorization","Matrix decomposition","Computer science","Cluster analysis","Decomposition","Focus (optics)","Factorization","Matrix (chemical analysis)","Power (physics)","Pattern recognition (psychology)","Artificial intelligence","Algorithm"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-04T00:59:03.525868Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}