Candidates for Synergies: Linear Discriminants versus Principal Components
Identifieur interne : 001391 ( Main/Exploration ); précédent : 001390; suivant : 001392Candidates for Synergies: Linear Discriminants versus Principal Components
Auteurs : Ramana Vinjamuri [États-Unis] ; Vrajeshri Patel [États-Unis] ; Michael Powell [États-Unis] ; Zhi-Hong Mao [États-Unis] ; Nathan Crone [États-Unis]Source :
- Computational Intelligence and Neuroscience [ 1687-5265 ] ; 2014.
Abstract
Movement primitives or synergies have been extracted from human hand movements using several matrix factorization, dimensionality reduction, and classification methods. Principal component analysis (PCA) is widely used to obtain the first few significant eigenvectors of covariance that explain most of the variance of the data. Linear discriminant analysis (LDA) is also used as a supervised learning method to classify the hand postures corresponding to the objects grasped. Synergies obtained using PCA are principal component vectors aligned with dominant variances. On the other hand, synergies obtained using LDA are linear discriminant vectors that separate the groups of variances. In this paper, time varying kinematic synergies in the human hand grasping movements were extracted using these two diametrically opposite methods and were evaluated in reconstructing natural and American sign language (ASL) postural movements. We used an unsupervised LDA (ULDA) to extract linear discriminants. The results suggest that PCA outperformed LDA. The uniqueness, advantages, and disadvantages of each of these methods in representing high-dimensional hand movements in reduced dimensions were discussed.
Url:
DOI: 10.1155/2014/373957
PubMed: 25143763
PubMed Central: 4124789
Affiliations:
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level="a" type="main">Candidates for Synergies: Linear Discriminants versus Principal Components</title><author><name sortKey="Vinjamuri, Ramana" sort="Vinjamuri, Ramana" uniqKey="Vinjamuri R" first="Ramana" last="Vinjamuri">Ramana Vinjamuri</name><affiliation wicri:level="2"><nlm:aff id="I1">Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030</wicri:regionArea><placeName><region type="state">New Jersey</region></placeName></affiliation></author><author><name sortKey="Patel, Vrajeshri" sort="Patel, Vrajeshri" uniqKey="Patel V" first="Vrajeshri" last="Patel">Vrajeshri Patel</name><affiliation wicri:level="2"><nlm:aff id="I1">Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA</nlm:aff><country 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Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15261, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Electrical and Computer Engineering and the Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15261</wicri:regionArea><placeName><region type="state">Pennsylvanie</region></placeName><orgName type="university">Université de Pittsburgh</orgName></affiliation></author><author><name sortKey="Crone, Nathan" sort="Crone, Nathan" uniqKey="Crone N" first="Nathan" last="Crone">Nathan Crone</name><affiliation wicri:level="2"><nlm:aff id="I4">Department of Neurology and Neurological Surgery, Johns Hopkins University, Baltimore, MD 21287, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Neurology and Neurological Surgery, Johns Hopkins University, Baltimore, MD 21287</wicri:regionArea><placeName><region type="state">Maryland</region></placeName></affiliation></author></analytic><series><title level="j">Computational Intelligence and Neuroscience</title><idno type="ISSN">1687-5265</idno><idno type="eISSN">1687-5273</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Movement primitives or synergies have been extracted from human hand movements using several matrix factorization, dimensionality reduction, and classification methods. Principal component analysis (PCA) is widely used to obtain the first few significant eigenvectors of covariance that explain most of the variance of the data. Linear discriminant analysis (LDA) is also used as a supervised learning method to classify the hand postures corresponding to the objects grasped. Synergies obtained using PCA are principal component vectors aligned with dominant variances. On the other hand, synergies obtained using LDA are linear discriminant vectors that separate the groups of variances. In this paper, time varying kinematic synergies in the human hand grasping movements were extracted using these two diametrically opposite methods and were evaluated in reconstructing natural and American sign language (ASL) postural movements. We used an unsupervised LDA (ULDA) to extract linear discriminants. The results suggest that PCA outperformed LDA. The uniqueness, advantages, and disadvantages of each of these methods in representing high-dimensional hand movements in reduced dimensions were discussed.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Bernstein, N" uniqKey="Bernstein N">N Bernstein</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tresch, Mc" uniqKey="Tresch M">MC Tresch</name></author><author><name sortKey="Saltiel, P" uniqKey="Saltiel P">P Saltiel</name></author><author><name sortKey="Bizzi, E" uniqKey="Bizzi E">E Bizzi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Vinjamuri, R" uniqKey="Vinjamuri R">R Vinjamuri</name></author><author><name sortKey="Weber, Dj" uniqKey="Weber D">DJ Weber</name></author><author><name sortKey="Mao, Zh" uniqKey="Mao Z">ZH Mao</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mackenzie, Cl" uniqKey="Mackenzie C">CL 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Drew</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Maryland</li><li>New Jersey</li><li>Pennsylvanie</li></region><orgName><li>Université de Pittsburgh</li></orgName></list><tree><country name="États-Unis"><region name="New Jersey"><name sortKey="Vinjamuri, Ramana" sort="Vinjamuri, Ramana" uniqKey="Vinjamuri R" first="Ramana" last="Vinjamuri">Ramana Vinjamuri</name></region><name sortKey="Crone, Nathan" sort="Crone, Nathan" uniqKey="Crone N" first="Nathan" last="Crone">Nathan Crone</name><name sortKey="Mao, Zhi Hong" sort="Mao, Zhi Hong" uniqKey="Mao Z" first="Zhi-Hong" last="Mao">Zhi-Hong Mao</name><name sortKey="Patel, Vrajeshri" sort="Patel, Vrajeshri" uniqKey="Patel V" first="Vrajeshri" last="Patel">Vrajeshri Patel</name><name sortKey="Powell, Michael" sort="Powell, Michael" uniqKey="Powell M" first="Michael" last="Powell">Michael Powell</name></country></tree></affiliations></record>Pour 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