| dc.contributor.author | Huang, Shao-Lun | |
| dc.contributor.author | Xu, Xiangxiang | |
| dc.contributor.author | Zheng, Lizhong | |
| dc.date.accessioned | 2021-10-27T20:22:36Z | |
| dc.date.available | 2021-10-27T20:22:36Z | |
| dc.date.issued | 2020 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/135239 | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
| dc.relation.isversionof | 10.1109/JSAIT.2020.2981538 | |
| dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
| dc.source | arXiv | |
| dc.title | An Information-theoretic Approach to Unsupervised Feature Selection for High-Dimensional Data | |
| dc.type | Article | |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| dc.relation.journal | IEEE Journal on Selected Areas in Information Theory | |
| dc.eprint.version | Original manuscript | |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
| eprint.status | http://purl.org/eprint/status/NonPeerReviewed | |
| dc.date.updated | 2021-01-25T19:15:55Z | |
| dspace.orderedauthors | Huang, S-L; Xu, X; Zheng, L | |
| dspace.date.submission | 2021-01-25T19:15:58Z | |
| mit.journal.volume | 1 | |
| mit.journal.issue | 1 | |
| mit.license | OPEN_ACCESS_POLICY | |
| mit.metadata.status | Authority Work and Publication Information Needed | |
