DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, Ilsuk | ko |
dc.contributor.author | Park, Cheolwoo | ko |
dc.contributor.author | Yoon, Young Joo | ko |
dc.contributor.author | Park, Changyi | ko |
dc.contributor.author | Kwon, Soon-Sun | ko |
dc.contributor.author | Choi, Hosik | ko |
dc.date.accessioned | 2023-02-10T01:00:09Z | - |
dc.date.available | 2023-02-10T01:00:09Z | - |
dc.date.created | 2021-07-13 | - |
dc.date.issued | 2023-02 | - |
dc.identifier.citation | JOURNAL OF APPLIED STATISTICS, v.50, no.3, pp.675 - 690 | - |
dc.identifier.issn | 0266-4763 | - |
dc.identifier.uri | http://hdl.handle.net/10203/305119 | - |
dc.description.abstract | The current large amounts of data and advanced technologies have produced new types of complex data, such as histogram-valued data. The paper focuses on classification problems when predictors are observed as or aggregated into histograms. Because conventional classification methods take vectors as input, a natural approach converts histograms into vector-valued data using summary values, such as the mean or median. However, this approach forgoes the distributional information available in histograms. To address this issue, we propose a margin-based classifier called support histogram machine (SHM) for histogram-valued data. We adopt the support vector machine framework and the Wasserstein-Kantorovich metric to measure distances between histograms. The proposed optimization problem is solved by a dual approach. We then test the proposed SHM via simulated and real examples and demonstrate its superior performance to summary-value-based methods. | - |
dc.language | English | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.title | Classification of histogram-valued data with support histogram machines | - |
dc.type | Article | - |
dc.identifier.wosid | 000668789200001 | - |
dc.identifier.scopusid | 2-s2.0-85109263163 | - |
dc.type.rims | ART | - |
dc.citation.volume | 50 | - |
dc.citation.issue | 3 | - |
dc.citation.beginningpage | 675 | - |
dc.citation.endingpage | 690 | - |
dc.citation.publicationname | JOURNAL OF APPLIED STATISTICS | - |
dc.identifier.doi | 10.1080/02664763.2021.1947996 | - |
dc.contributor.localauthor | Park, Cheolwoo | - |
dc.contributor.nonIdAuthor | Kang, Ilsuk | - |
dc.contributor.nonIdAuthor | Yoon, Young Joo | - |
dc.contributor.nonIdAuthor | Park, Changyi | - |
dc.contributor.nonIdAuthor | Kwon, Soon-Sun | - |
dc.contributor.nonIdAuthor | Choi, Hosik | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Support vector machines | - |
dc.subject.keywordAuthor | symbolic data | - |
dc.subject.keywordAuthor | Wasserstein-Kantorovich metric | - |
dc.subject.keywordPlus | VECTOR MACHINES | - |
dc.subject.keywordPlus | DISSIMILARITY MEASURES | - |
dc.subject.keywordPlus | DISCRIMINANT-ANALYSIS | - |
dc.subject.keywordPlus | REGULARIZATION | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | KNOWLEDGE | - |
dc.subject.keywordPlus | DISTANCE | - |
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