Constructing Measures of Sparsity
This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsity that is as broad as possible, so that it generates all the various measures that are useful in practice, but narrow enough that the fundamental properties of generalized sparsity still hold. As we w...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on knowledge and data engineering 2022-08, Vol.34 (8), p.1-1 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1 |
|---|---|
| container_issue | 8 |
| container_start_page | 1 |
| container_title | IEEE transactions on knowledge and data engineering |
| container_volume | 34 |
| creator | Pastor, Giancarlo Mora-Jimenez, Inmaculada Jantti, Riku Caamano, Antonio |
| description | This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsity that is as broad as possible, so that it generates all the various measures that are useful in practice, but narrow enough that the fundamental properties of generalized sparsity still hold. As we work through the various ways of demonstrating the advantageous properties of sparsity, we illustrate its meaning from geometrical and operational perspectives. Thereafter, we construct specific measures of sparsity which are successfully qualified in complexity analysis and sparse optimization scenarios. Overall, our main objective is to construct measures of sparsity that will facilitate and enhance the design of the next innovative sensing technologies. |
| doi_str_mv | 10.1109/TKDE.2020.3029851 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TKDE_2020_3029851</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9219175</ieee_id><sourcerecordid>2686300934</sourcerecordid><originalsourceid>FETCH-LOGICAL-c266t-143af5db952ad9df44827a5d5b743b3ad6f436b54f73cd69d5d6f0fdcaf0a4783</originalsourceid><addsrcrecordid>eNo9kMtOwzAQRS0EEqXwAYhNEOuU8dteolAeoogFZW05sY1SQRLsZNG_b6JUrOZqdO6MdBC6xrDCGPT99u1xvSJAYEWBaMXxCVpgzlVOsManYwaGc0aZPEcXKe0AQEmFF-i2aJvUx6Hq6-Y7e_c2DdGnrA3ZZ2djqvv9JToL9if5q-Ncoq-n9bZ4yTcfz6_FwyaviBB9jhm1gbtSc2KddoExRaTljpeS0ZJaJwKjouQsSFo5oR0fNxBcZQNYJhVdorv5bhfbv8Gn3uzaITbjS0OEEhRAUzZSeKaq2KYUfTBdrH9t3BsMZjJhJhNmMmGOJsbOzdypvff_vJ7MSE4PUJNY6A</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2686300934</pqid></control><display><type>article</type><title>Constructing Measures of Sparsity</title><source>IEEE Electronic Library (IEL)</source><creator>Pastor, Giancarlo ; Mora-Jimenez, Inmaculada ; Jantti, Riku ; Caamano, Antonio</creator><creatorcontrib>Pastor, Giancarlo ; Mora-Jimenez, Inmaculada ; Jantti, Riku ; Caamano, Antonio</creatorcontrib><description>This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsity that is as broad as possible, so that it generates all the various measures that are useful in practice, but narrow enough that the fundamental properties of generalized sparsity still hold. As we work through the various ways of demonstrating the advantageous properties of sparsity, we illustrate its meaning from geometrical and operational perspectives. Thereafter, we construct specific measures of sparsity which are successfully qualified in complexity analysis and sparse optimization scenarios. Overall, our main objective is to construct measures of sparsity that will facilitate and enhance the design of the next innovative sensing technologies.</description><identifier>ISSN: 1041-4347</identifier><identifier>EISSN: 1558-2191</identifier><identifier>DOI: 10.1109/TKDE.2020.3029851</identifier><identifier>CODEN: ITKEEH</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Axioms ; complexity ; Complexity theory ; concentration ; diversity ; effective ; Entropy ; fairness ; generalized convexity ; Geometry ; Heuristic algorithms ; inequality ; information ; Optimization ; Sensors ; sparsity ; uncertainty</subject><ispartof>IEEE transactions on knowledge and data engineering, 2022-08, Vol.34 (8), p.1-1</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c266t-143af5db952ad9df44827a5d5b743b3ad6f436b54f73cd69d5d6f0fdcaf0a4783</citedby><cites>FETCH-LOGICAL-c266t-143af5db952ad9df44827a5d5b743b3ad6f436b54f73cd69d5d6f0fdcaf0a4783</cites><orcidid>0000-0002-9467-5185 ; 0000-0002-5398-2381 ; 0000-0001-6532-5314 ; 0000-0003-0735-367X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9219175$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,777,781,793,27905,27906,54739</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9219175$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Pastor, Giancarlo</creatorcontrib><creatorcontrib>Mora-Jimenez, Inmaculada</creatorcontrib><creatorcontrib>Jantti, Riku</creatorcontrib><creatorcontrib>Caamano, Antonio</creatorcontrib><title>Constructing Measures of Sparsity</title><title>IEEE transactions on knowledge and data engineering</title><addtitle>TKDE</addtitle><description>This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsity that is as broad as possible, so that it generates all the various measures that are useful in practice, but narrow enough that the fundamental properties of generalized sparsity still hold. As we work through the various ways of demonstrating the advantageous properties of sparsity, we illustrate its meaning from geometrical and operational perspectives. Thereafter, we construct specific measures of sparsity which are successfully qualified in complexity analysis and sparse optimization scenarios. Overall, our main objective is to construct measures of sparsity that will facilitate and enhance the design of the next innovative sensing technologies.</description><subject>Axioms</subject><subject>complexity</subject><subject>Complexity theory</subject><subject>concentration</subject><subject>diversity</subject><subject>effective</subject><subject>Entropy</subject><subject>fairness</subject><subject>generalized convexity</subject><subject>Geometry</subject><subject>Heuristic algorithms</subject><subject>inequality</subject><subject>information</subject><subject>Optimization</subject><subject>Sensors</subject><subject>sparsity</subject><subject>uncertainty</subject><issn>1041-4347</issn><issn>1558-2191</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMtOwzAQRS0EEqXwAYhNEOuU8dteolAeoogFZW05sY1SQRLsZNG_b6JUrOZqdO6MdBC6xrDCGPT99u1xvSJAYEWBaMXxCVpgzlVOsManYwaGc0aZPEcXKe0AQEmFF-i2aJvUx6Hq6-Y7e_c2DdGnrA3ZZ2djqvv9JToL9if5q-Ncoq-n9bZ4yTcfz6_FwyaviBB9jhm1gbtSc2KddoExRaTljpeS0ZJaJwKjouQsSFo5oR0fNxBcZQNYJhVdorv5bhfbv8Gn3uzaITbjS0OEEhRAUzZSeKaq2KYUfTBdrH9t3BsMZjJhJhNmMmGOJsbOzdypvff_vJ7MSE4PUJNY6A</recordid><startdate>20220801</startdate><enddate>20220801</enddate><creator>Pastor, Giancarlo</creator><creator>Mora-Jimenez, Inmaculada</creator><creator>Jantti, Riku</creator><creator>Caamano, Antonio</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-9467-5185</orcidid><orcidid>https://orcid.org/0000-0002-5398-2381</orcidid><orcidid>https://orcid.org/0000-0001-6532-5314</orcidid><orcidid>https://orcid.org/0000-0003-0735-367X</orcidid></search><sort><creationdate>20220801</creationdate><title>Constructing Measures of Sparsity</title><author>Pastor, Giancarlo ; Mora-Jimenez, Inmaculada ; Jantti, Riku ; Caamano, Antonio</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c266t-143af5db952ad9df44827a5d5b743b3ad6f436b54f73cd69d5d6f0fdcaf0a4783</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Axioms</topic><topic>complexity</topic><topic>Complexity theory</topic><topic>concentration</topic><topic>diversity</topic><topic>effective</topic><topic>Entropy</topic><topic>fairness</topic><topic>generalized convexity</topic><topic>Geometry</topic><topic>Heuristic algorithms</topic><topic>inequality</topic><topic>information</topic><topic>Optimization</topic><topic>Sensors</topic><topic>sparsity</topic><topic>uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pastor, Giancarlo</creatorcontrib><creatorcontrib>Mora-Jimenez, Inmaculada</creatorcontrib><creatorcontrib>Jantti, Riku</creatorcontrib><creatorcontrib>Caamano, Antonio</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on knowledge and data engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pastor, Giancarlo</au><au>Mora-Jimenez, Inmaculada</au><au>Jantti, Riku</au><au>Caamano, Antonio</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Constructing Measures of Sparsity</atitle><jtitle>IEEE transactions on knowledge and data engineering</jtitle><stitle>TKDE</stitle><date>2022-08-01</date><risdate>2022</risdate><volume>34</volume><issue>8</issue><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>1041-4347</issn><eissn>1558-2191</eissn><coden>ITKEEH</coden><abstract>This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsity that is as broad as possible, so that it generates all the various measures that are useful in practice, but narrow enough that the fundamental properties of generalized sparsity still hold. As we work through the various ways of demonstrating the advantageous properties of sparsity, we illustrate its meaning from geometrical and operational perspectives. Thereafter, we construct specific measures of sparsity which are successfully qualified in complexity analysis and sparse optimization scenarios. Overall, our main objective is to construct measures of sparsity that will facilitate and enhance the design of the next innovative sensing technologies.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TKDE.2020.3029851</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-9467-5185</orcidid><orcidid>https://orcid.org/0000-0002-5398-2381</orcidid><orcidid>https://orcid.org/0000-0001-6532-5314</orcidid><orcidid>https://orcid.org/0000-0003-0735-367X</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1041-4347 |
| ispartof | IEEE transactions on knowledge and data engineering, 2022-08, Vol.34 (8), p.1-1 |
| issn | 1041-4347 1558-2191 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_TKDE_2020_3029851 |
| source | IEEE Electronic Library (IEL) |
| subjects | Axioms complexity Complexity theory concentration diversity effective Entropy fairness generalized convexity Geometry Heuristic algorithms inequality information Optimization Sensors sparsity uncertainty |
| title | Constructing Measures of Sparsity |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T14%3A08%3A24IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Constructing%20Measures%20of%20Sparsity&rft.jtitle=IEEE%20transactions%20on%20knowledge%20and%20data%20engineering&rft.au=Pastor,%20Giancarlo&rft.date=2022-08-01&rft.volume=34&rft.issue=8&rft.spage=1&rft.epage=1&rft.pages=1-1&rft.issn=1041-4347&rft.eissn=1558-2191&rft.coden=ITKEEH&rft_id=info:doi/10.1109/TKDE.2020.3029851&rft_dat=%3Cproquest_RIE%3E2686300934%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2686300934&rft_id=info:pmid/&rft_ieee_id=9219175&rfr_iscdi=true |