{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:44:14Z","timestamp":1740149054069,"version":"3.37.3"},"reference-count":26,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T00:00:00Z","timestamp":1611792000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Cultivating Science Foundation of Taizhou University","award":["2019PY014","20ny13","LQ21F020001","LQ21A010001","1901gy20"],"award-info":[{"award-number":["2019PY014","20ny13","LQ21F020001","LQ21A010001","1901gy20"]}]},{"name":"Agricultural Science and Technology Project of Taizhou","award":["2019PY014","20ny13","LQ21F020001","LQ21A010001","1901gy20"],"award-info":[{"award-number":["2019PY014","20ny13","LQ21F020001","LQ21A010001","1901gy20"]}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["2019PY014","20ny13","LQ21F020001","LQ21A010001","1901gy20"],"award-info":[{"award-number":["2019PY014","20ny13","LQ21F020001","LQ21A010001","1901gy20"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018552","name":"Taizhou Science and Technology Project","doi-asserted-by":"crossref","award":["2019PY014","20ny13","LQ21F020001","LQ21A010001","1901gy20"],"award-info":[{"award-number":["2019PY014","20ny13","LQ21F020001","LQ21A010001","1901gy20"]}],"id":[{"id":"10.13039\/501100018552","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2021,1,28]]},"abstract":"<jats:p>Generally, multimodality data contain different potential information available and are capable of providing an enhanced analytical result compared to monosource data. The way to combine the data plays a crucial role in multimodality data analysis which is worth investigating. Multimodality clustering, which seeks a partition of the data in multiple views, has attracted considerable attention, for example, robust multiview spectral clustering (RMSC) explicitly handles the possible noise in the transition probability matrices associated with different views. Spectral clustering algorithm embeds the input data into a low-dimensional representation by dividing the clustering problem into <jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mi>k<\/a:mi>\n                     <\/a:math>\n                  <\/jats:inline-formula> subproblems, and the corresponding eigenvalue reflects the loss of each subproblem. So, the eigenvalues of the Laplacian matrix should be treated differently, while RMSC regularizes each singular value equally when recovering the low-rank matrix. In this paper, we propose a multimodality clustering algorithm which recovers the low-rank matrix by weighted nuclear norm minimization. We also propose a method to evaluate the weight vector by learning a shared low-rank matrix. In our experiments, we use several real-world datasets to test our method, and experimental results show that the proposed method has a better performance than other baselines.<\/jats:p>","DOI":"10.1155\/2021\/6662989","type":"journal-article","created":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T21:50:10Z","timestamp":1611870610000},"page":"1-7","source":"Crossref","is-referenced-by-count":0,"title":["Weighted Nuclear Norm Minimization on Multimodality Clustering"],"prefix":"10.1155","volume":"2021","author":[{"given":"Lei","family":"Du","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Taizhou University, Taizhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songsong","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Taizhou University, Taizhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6135-1619","authenticated-orcid":true,"given":"Haifeng","family":"Song","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Taizhou University, Taizhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuelong","family":"Chuang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Taizhou University, Taizhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingying","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Taizhou University, Taizhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1002\/sec.1042"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1002\/sec.1656"},{"key":"3","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.jss.2015.12.017","article-title":"Clustering and splitting charging algorithms for large scaled wireless rechargeable sensor networks","volume":"113","year":"2016","journal-title":"Journal of Systems and Software"},{"issue":"6","key":"4","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.927466","article-title":"A modified version of the k-means algorithm with a distance based on cluster symmetry","volume":"23","author":"M.-C. 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