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Year 2021

Volume: 4 , Issue: 4

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IP International Journal of Aesthetic and Health Rejuvenation


Canonical correlation of emg dystonic writers cramp - A study with micro electrode recordings for computational simulation and statistical models


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Original Article

Author Details : Venkateshwarla Rama Raju*

Volume : 4, Issue : 4, Year : 2021

Article Page : 89-92



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Abstract

In recent years, the advances in data collection and statistical analysis promotes canonical correlation analysis which is designated as C.C.A for multidimensionality and for multi-dimensional scaling purposes’ offered for more advanced clinical research for effective medical diagnosis and also for outstanding results in medical prognostics. The C.C.A is the most important technique for dual-set data dimensionality reduction such that the correlation between the pairwise variables in the common subspace is equally or reciprocally or maximally increased. Over the 100 years of developments, a number of indeed a numerous myriad of C.C.A models and modalities, and computational simulations and statistical models with canonical correlation methods have got and ought to been suggested in accordance with as well as with artificial intelligence dissimilar machine learning mechanisms such as supervised (classification for instance random tree classification) learning and unsupervised learning (for instance principal components and various clustering algorithms like common and hard and soft and hierarchical clustering k-means and k-medoids)) techniques. Yet, the subject, i.e., field of research requires an intuitive study, analysis for the state-of-art developments.


Keywords: Canonical correlation analysis C C A, Multidimensional scaling, Multidimensionality


How to cite : Raju V R, Canonical correlation of emg dystonic writers cramp - A study with micro electrode recordings for computational simulation and statistical models. Int J Aesthet Health Rejuvenation 2021;4(4):89-92

Copyright © 2021 by author(s) and Int J Aesthet Health Rejuvenation. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License (creativecommons.org)