Candidates for Synergies: Linear Discriminants versus Principal Components
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2014-07-17
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Ramana Vinjamuri, Vrajeshri Patel, Michael Powell, Zhi-Hong Mao, Nathan Crone, "Candidates for Synergies: Linear Discriminants versus Principal Components", Computational Intelligence and Neuroscience, vol. 2014, Article ID 373957, 10 pages, 2014. https://doi.org/10.1155/2014/373957
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Attribution 4.0 International
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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.