{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T03:14:23Z","timestamp":1770347663664,"version":"3.49.0"},"reference-count":16,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,10]]},"DOI":"10.1109\/acssc.2018.8645150","type":"proceedings-article","created":{"date-parts":[[2019,3,18]],"date-time":"2019-03-18T20:31:40Z","timestamp":1552941100000},"page":"1003-1007","source":"Crossref","is-referenced-by-count":6,"title":["Graph Heat Mixture Model Learning"],"prefix":"10.1109","author":[{"given":"Hermina Petric","family":"Maretic","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mireille El","family":"Gheche","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pascal","family":"Frossard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2017.2731051"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2017.7953413"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2017.7952672"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2017.8335493"},{"key":"ref14","article-title":"Graph laplacian mixture model","author":"maretic","year":"2018","journal-title":"arXiv preprint arXiv 1810 10053"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1137\/080716542"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0708838104"},{"key":"ref4","first-page":"920","article-title":"How to learn a graph from smooth signals","author":"kalofolias","year":"2016","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2016.2602809"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/1458082.1458115"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2017.2726975"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/DSW.2018.8439913"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2017.2731164"},{"key":"ref2","article-title":"Learning graphs from data: A signal representation perspective","author":"dong","year":"2018","journal-title":"arXiv preprint arXiv 1806 00848"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.2307\/2528966"},{"key":"ref9","article-title":"Graph learning from filtered signals: Graph system and diffusion kernel identification","author":"egilmez","year":"2018","journal-title":"arXiv preprint arXiv 1803 02553"}],"event":{"name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","location":"Pacific Grove, CA, USA","start":{"date-parts":[[2018,10,28]]},"end":{"date-parts":[[2018,10,31]]}},"container-title":["2018 52nd Asilomar Conference on Signals, Systems, and Computers"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8638648\/8645065\/08645150.pdf?arnumber=8645150","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T03:29:40Z","timestamp":1598239780000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8645150\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10]]},"references-count":16,"URL":"https:\/\/doi.org\/10.1109\/acssc.2018.8645150","relation":{},"subject":[],"published":{"date-parts":[[2018,10]]}}}