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tpami.2020.2974746.pdf | 3.36 MB | Adobe PDF | 見る/開く |
タイトル: | Learning on Hypergraphs with Sparsity |
著者: | Nguyen, Hao Canh Mamitsuka, Hiroshi |
著者名の別形: | 馬見塚, 拓 |
キーワード: | Sparse learning learning on hypergraphs learning on graphs sparsistency |
発行日: | Aug-2021 |
出版者: | Institute of Electrical and Electronics Engineers (IEEE) |
誌名: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
巻: | 43 |
号: | 8 |
開始ページ: | 2710 |
終了ページ: | 2722 |
抄録: | Hypergraph is a general way of representing high-order relations on a set of objects. It is a generalization of graph, in which only pairwise relations can be represented. It finds applications in various domains where relationships of more than two objects are observed. On a hypergraph, as a generalization of graph, one wishes to learn a smooth function with respect to its topology. A fundamental issue is to find suitable smoothness measures of functions on the nodes of a graph/hypergraph. We show a general framework that generalizes previously proposed smoothness measures and also generates new ones. To address the problem of irrelevant or noisy data, we wish to incorporate sparse learning framework into learning on hypergraphs. We propose sparsely smooth formulations that learn smooth functions and induce sparsity on hypergraphs at both hyperedge and node levels. We show their properties and sparse support recovery results. We conduct experiments to show that our sparsely smooth models are beneficial to learning irrelevant and noisy data, and usually give similar or improved performances compared to dense models. |
著作権等: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 |
URI: | http://hdl.handle.net/2433/265955 |
DOI(出版社版): | 10.1109/tpami.2020.2974746 |
PubMed ID: | 32086195 |
出現コレクション: | 学術雑誌掲載論文等 |
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