Key point selection and clustering of swimmer coordination through Sparse Fisher-EM

01/07/2014
by   John Komar, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/08/2020

A Bayesian Fisher-EM algorithm for discriminative Gaussian subspace clustering

High-dimensional data clustering has become and remains a challenging ta...
research
01/19/2017

Fisher consistency for prior probability shift

We introduce Fisher consistency in the sense of unbiasedness as a desira...
research
12/30/2022

Mixture of von Mises-Fisher distribution with sparse prototypes

Mixtures of von Mises-Fisher distributions can be used to cluster data o...
research
07/02/2019

A flexible EM-like clustering algorithm for noisy data

We design a new robust clustering algorithm that can deal efficiently wi...
research
01/12/2011

Simultaneous model-based clustering and visualization in the Fisher discriminative subspace

Clustering in high-dimensional spaces is nowadays a recurrent problem in...
research
01/30/2014

Sparse Bayesian Unsupervised Learning

This paper is about variable selection, clustering and estimation in an ...
research
12/23/2020

Deep manifold learning reveals hidden dynamics of proteasome autoregulation

The 2.5-MDa 26S proteasome maintains proteostasis and regulates myriad c...

Please sign up or login with your details

Forgot password? Click here to reset