Loading…

Performance Bounds of Quaternion Estimators

The quaternion widely linear (WL) estimator has been recently introduced for optimal second-order modeling of the generality of quaternion data, both second-order circular (proper) and second-order noncircular (improper). Experimental evidence exists of its performance advantage over the conventiona...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2015-12, Vol.26 (12), p.3287-3292
Main Authors: Yili Xia, Jahanchahi, Cyrus, Nitta, Tohru, Mandic, Danilo P.
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c487t-c6fa1a95a2d5e4d4ad0ff890023a5ff4c4fda8f6313a57f083666c1bad550dd63
cites cdi_FETCH-LOGICAL-c487t-c6fa1a95a2d5e4d4ad0ff890023a5ff4c4fda8f6313a57f083666c1bad550dd63
container_end_page 3292
container_issue 12
container_start_page 3287
container_title IEEE transaction on neural networks and learning systems
container_volume 26
creator Yili Xia
Jahanchahi, Cyrus
Nitta, Tohru
Mandic, Danilo P.
description The quaternion widely linear (WL) estimator has been recently introduced for optimal second-order modeling of the generality of quaternion data, both second-order circular (proper) and second-order noncircular (improper). Experimental evidence exists of its performance advantage over the conventional strictly linear (SL) as well as the semi-WL (SWL) estimators for improper data. However, rigorous theoretical and practical performance bounds are still missing in the literature, yet this is crucial for the development of quaternion valued learning systems for 3-D and 4-D data. To this end, based on the orthogonality principle, we introduce a rigorous closed-form solution to quantify the degree of performance benefits, in terms of the mean square error, obtained when using the WL models. The cases when the optimal WL estimation can simplify into the SWL or the SL estimation are also discussed.
doi_str_mv 10.1109/TNNLS.2015.2388782
format article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_proquest_journals_1738833826</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7021954</ieee_id><sourcerecordid>1736418484</sourcerecordid><originalsourceid>FETCH-LOGICAL-c487t-c6fa1a95a2d5e4d4ad0ff890023a5ff4c4fda8f6313a57f083666c1bad550dd63</originalsourceid><addsrcrecordid>eNpdkMtKAzEUhoMottS-gIIMuBFkau6TWWqpFyhVsYK7kOYCUzqTmswsfHsztnZhNieHfOfn5APgHMEJQrC8XS4W8_cJhohNMBGiEPgIDDHiOO_b48O9-ByAcYxrmA6HjNPyFAxwqoQiPgQ3rzY4H2rVaJvd-64xMfMue-tUa0NT-SabxbaqVetDPAMnTm2iHe_rCHw8zJbTp3z-8vg8vZvnmoqizTV3CqmSKWyYpYYqA50TJYSYKOYc1dQZJRwnKPWFg4JwzjVaKcMYNIaTEbje5W6D_-psbGVdRW03G9VY30WJCsIpElTQhF79Q9e-C03arqeEIETgPhDvKB18jME6uQ3pT-FbIih7m_LXpuxtyr3NNHS5j-5WtTWHkT93CbjYAZW19vBcQIxKRskPeW13dg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1738833826</pqid></control><display><type>article</type><title>Performance Bounds of Quaternion Estimators</title><source>IEEE Electronic Library (IEL) Journals</source><creator>Yili Xia ; Jahanchahi, Cyrus ; Nitta, Tohru ; Mandic, Danilo P.</creator><creatorcontrib>Yili Xia ; Jahanchahi, Cyrus ; Nitta, Tohru ; Mandic, Danilo P.</creatorcontrib><description>The quaternion widely linear (WL) estimator has been recently introduced for optimal second-order modeling of the generality of quaternion data, both second-order circular (proper) and second-order noncircular (improper). Experimental evidence exists of its performance advantage over the conventional strictly linear (SL) as well as the semi-WL (SWL) estimators for improper data. However, rigorous theoretical and practical performance bounds are still missing in the literature, yet this is crucial for the development of quaternion valued learning systems for 3-D and 4-D data. To this end, based on the orthogonality principle, we introduce a rigorous closed-form solution to quantify the degree of performance benefits, in terms of the mean square error, obtained when using the WL models. The cases when the optimal WL estimation can simplify into the SWL or the SL estimation are also discussed.</description><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNNLS.2015.2388782</identifier><identifier>PMID: 25643416</identifier><identifier>CODEN: ITNNAL</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Analytical models ; Augmented quaternion statistics ; Covariance matrices ; Estimation ; Learning systems ; mean square error (MSE) ; Mean square errors ; quaternion widely linear (WL) model ; Quaternions ; semi-WL (SWL) model ; Vectors</subject><ispartof>IEEE transaction on neural networks and learning systems, 2015-12, Vol.26 (12), p.3287-3292</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Dec 2015</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c487t-c6fa1a95a2d5e4d4ad0ff890023a5ff4c4fda8f6313a57f083666c1bad550dd63</citedby><cites>FETCH-LOGICAL-c487t-c6fa1a95a2d5e4d4ad0ff890023a5ff4c4fda8f6313a57f083666c1bad550dd63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7021954$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,54796</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25643416$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yili Xia</creatorcontrib><creatorcontrib>Jahanchahi, Cyrus</creatorcontrib><creatorcontrib>Nitta, Tohru</creatorcontrib><creatorcontrib>Mandic, Danilo P.</creatorcontrib><title>Performance Bounds of Quaternion Estimators</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNNLS</addtitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><description>The quaternion widely linear (WL) estimator has been recently introduced for optimal second-order modeling of the generality of quaternion data, both second-order circular (proper) and second-order noncircular (improper). Experimental evidence exists of its performance advantage over the conventional strictly linear (SL) as well as the semi-WL (SWL) estimators for improper data. However, rigorous theoretical and practical performance bounds are still missing in the literature, yet this is crucial for the development of quaternion valued learning systems for 3-D and 4-D data. To this end, based on the orthogonality principle, we introduce a rigorous closed-form solution to quantify the degree of performance benefits, in terms of the mean square error, obtained when using the WL models. The cases when the optimal WL estimation can simplify into the SWL or the SL estimation are also discussed.</description><subject>Analytical models</subject><subject>Augmented quaternion statistics</subject><subject>Covariance matrices</subject><subject>Estimation</subject><subject>Learning systems</subject><subject>mean square error (MSE)</subject><subject>Mean square errors</subject><subject>quaternion widely linear (WL) model</subject><subject>Quaternions</subject><subject>semi-WL (SWL) model</subject><subject>Vectors</subject><issn>2162-237X</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNpdkMtKAzEUhoMottS-gIIMuBFkau6TWWqpFyhVsYK7kOYCUzqTmswsfHsztnZhNieHfOfn5APgHMEJQrC8XS4W8_cJhohNMBGiEPgIDDHiOO_b48O9-ByAcYxrmA6HjNPyFAxwqoQiPgQ3rzY4H2rVaJvd-64xMfMue-tUa0NT-SabxbaqVetDPAMnTm2iHe_rCHw8zJbTp3z-8vg8vZvnmoqizTV3CqmSKWyYpYYqA50TJYSYKOYc1dQZJRwnKPWFg4JwzjVaKcMYNIaTEbje5W6D_-psbGVdRW03G9VY30WJCsIpElTQhF79Q9e-C03arqeEIETgPhDvKB18jME6uQ3pT-FbIih7m_LXpuxtyr3NNHS5j-5WtTWHkT93CbjYAZW19vBcQIxKRskPeW13dg</recordid><startdate>20151201</startdate><enddate>20151201</enddate><creator>Yili Xia</creator><creator>Jahanchahi, Cyrus</creator><creator>Nitta, Tohru</creator><creator>Mandic, Danilo P.</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>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QP</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TK</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20151201</creationdate><title>Performance Bounds of Quaternion Estimators</title><author>Yili Xia ; Jahanchahi, Cyrus ; Nitta, Tohru ; Mandic, Danilo P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c487t-c6fa1a95a2d5e4d4ad0ff890023a5ff4c4fda8f6313a57f083666c1bad550dd63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Analytical models</topic><topic>Augmented quaternion statistics</topic><topic>Covariance matrices</topic><topic>Estimation</topic><topic>Learning systems</topic><topic>mean square error (MSE)</topic><topic>Mean square errors</topic><topic>quaternion widely linear (WL) model</topic><topic>Quaternions</topic><topic>semi-WL (SWL) model</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Yili Xia</creatorcontrib><creatorcontrib>Jahanchahi, Cyrus</creatorcontrib><creatorcontrib>Nitta, Tohru</creatorcontrib><creatorcontrib>Mandic, Danilo P.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium &amp; Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yili Xia</au><au>Jahanchahi, Cyrus</au><au>Nitta, Tohru</au><au>Mandic, Danilo P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Performance Bounds of Quaternion Estimators</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNNLS</stitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><date>2015-12-01</date><risdate>2015</risdate><volume>26</volume><issue>12</issue><spage>3287</spage><epage>3292</epage><pages>3287-3292</pages><issn>2162-237X</issn><eissn>2162-2388</eissn><coden>ITNNAL</coden><abstract>The quaternion widely linear (WL) estimator has been recently introduced for optimal second-order modeling of the generality of quaternion data, both second-order circular (proper) and second-order noncircular (improper). Experimental evidence exists of its performance advantage over the conventional strictly linear (SL) as well as the semi-WL (SWL) estimators for improper data. However, rigorous theoretical and practical performance bounds are still missing in the literature, yet this is crucial for the development of quaternion valued learning systems for 3-D and 4-D data. To this end, based on the orthogonality principle, we introduce a rigorous closed-form solution to quantify the degree of performance benefits, in terms of the mean square error, obtained when using the WL models. The cases when the optimal WL estimation can simplify into the SWL or the SL estimation are also discussed.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>25643416</pmid><doi>10.1109/TNNLS.2015.2388782</doi><tpages>6</tpages></addata></record>
fulltext fulltext
identifier ISSN: 2162-237X
ispartof IEEE transaction on neural networks and learning systems, 2015-12, Vol.26 (12), p.3287-3292
issn 2162-237X
2162-2388
language eng
recordid cdi_proquest_journals_1738833826
source IEEE Electronic Library (IEL) Journals
subjects Analytical models
Augmented quaternion statistics
Covariance matrices
Estimation
Learning systems
mean square error (MSE)
Mean square errors
quaternion widely linear (WL) model
Quaternions
semi-WL (SWL) model
Vectors
title Performance Bounds of Quaternion Estimators
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T14%3A12%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Performance%20Bounds%20of%20Quaternion%20Estimators&rft.jtitle=IEEE%20transaction%20on%20neural%20networks%20and%20learning%20systems&rft.au=Yili%20Xia&rft.date=2015-12-01&rft.volume=26&rft.issue=12&rft.spage=3287&rft.epage=3292&rft.pages=3287-3292&rft.issn=2162-237X&rft.eissn=2162-2388&rft.coden=ITNNAL&rft_id=info:doi/10.1109/TNNLS.2015.2388782&rft_dat=%3Cproquest_pubme%3E1736418484%3C/proquest_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c487t-c6fa1a95a2d5e4d4ad0ff890023a5ff4c4fda8f6313a57f083666c1bad550dd63%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1738833826&rft_id=info:pmid/25643416&rft_ieee_id=7021954&rfr_iscdi=true