Key Point Selection and Clustering of Swimmer Coordination Through Sparse Fisher-EM
نویسندگان
چکیده
To answer the existence of optimal swimmer learning/teaching strategies, this work introduces a two-level clustering in order to analyze temporal dynamics of motor learning in breaststroke swimming. Each level have been performed through Sparse Fisher-EM, a unsupervised framework which can be applied efficiently on large and correlated datasets. The induced sparsity selects key points of the coordination phase without any prior knowledge.
منابع مشابه
The Investigation of Relationship Between the Joints Range of Motion and Time of 50, 100 and 200m Breaststroke Swimming in 12-13 Years Elite Swimmer Boys Participated in the National Championship of the Country Selection in 2016 in Tehran
Background and Objectives: Finding the relationship between the joints range of motion and swimming time is important, so the aim of the present study was to investigate the relationship between the joints range of motion and time of 50, 100 and 200m breaststroke swimming in 12-13 years elite swimmer boys. Materials and Methods: In this descriptive study, subjects were selected from 111 e...
متن کاملTheoretical and practical considerations on the convergence properties of the Fisher-EM algorithm
The Fisher-EM algorithm has been recently proposed in [4] for the simultaneous visualization and clustering of high-dimensional data. It is based on a latent mixture model which fits the data into a latent discriminative subspace with a low intrinsic dimension. Although the Fisher-EM algorithm is based on the EM algorithm, it does not respect at a first glance all conditions of the EM convergen...
متن کاملA Hybrid Grey based Two Steps Clustering and Firefly Algorithm for Portfolio Selection
Considering the concept of clustering, the main idea of the present study is based on the fact that all stocks for choosing and ranking will not be necessarily in one cluster. Taking the mentioned point into account, this study aims at offering a new methodology for making decisions concerning the formation of a portfolio of stocks in the stock market. To meet this end, Multiple-Criteria Decisi...
متن کاملPenalized Model-Based Clustering with Application to Variable Selection
Variable selection in clustering analysis is both challenging and important. In the context of modelbased clustering analysis with a common diagonal covariance matrix, which is especially suitable for “high dimension, low sample size” settings, we propose a penalized likelihood approach with an L1 penalty function, automatically realizing variable selection via thresholding and delivering a spa...
متن کاملSparse Convex Clustering
Convex clustering, a convex relaxation of k-means clustering and hierarchical clustering, has drawn recent attentions since it nicely addresses the instability issue of traditional nonconvex clustering methods. Although its computational and statistical properties have been recently studied, the performance of convex clustering has not yet been investigated in the high-dimensional clustering sc...
متن کامل