Table S1 shows the SOM parameters that were used in the main manuscript. These correspond to the default set of options of the SOM Toolbox.

Similar documents
A Method for Comparing Self-Organizing Maps: Case Studies of Banking and Linguistic Data

International Journal of ChemTech Research CODEN (USA): IJCRGG ISSN: Vol.8, No.9, pp 12-19, 2015

VISUALIZATION OF GEOSPATIAL DATA BY COMPONENT PLANES AND U-MATRIX

y(n) Time Series Data

Data mining for household water consumption analysis using selforganizing

a subset of these N input variables. A naive method is to train a new neural network on this subset to determine this performance. Instead of the comp

Reduction of complex models using data-mining and nonlinear projection techniques

Visualization of Clusters in Geo-referenced Data Using Three-dimensional Self-Organizing Maps

Jae-Bong Lee 1 and Bernard A. Megrey 2. International Symposium on Climate Change Effects on Fish and Fisheries

A Reservoir Sampling Algorithm with Adaptive Estimation of Conditional Expectation

The Self-Organizing Map and it s variants as tools for geodemographical data analysis: the case of Lisbon s Metropolitan Area

Neuroinformatics. Marcus Kaiser. Week 10: Cortical maps and competitive population coding (textbook chapter 7)!

Applying Visual Analytics Methods to Spatial Time Series Data: Forest Fires, Phone Calls,

Prototype-based Analysis of GAMA Galaxy Catalogue Data

Scuola di Calcolo Scientifico con MATLAB (SCSM) 2017 Palermo 31 Luglio - 4 Agosto 2017

Estimating the accuracy of a hypothesis Setting. Assume a binary classification setting

Matching the dimensionality of maps with that of the data

CMSC 422 Introduction to Machine Learning Lecture 4 Geometry and Nearest Neighbors. Furong Huang /

Analysis of Interest Rate Curves Clustering Using Self-Organising Maps

Robust cartogram visualization of outliers in manifold leaning

ARTIFICIAL NEURAL NETWORKS گروه مطالعاتي 17 بهار 92

How generative models develop in predictive processing

Optimising the topology of complex neural networks

Self-Organization by Optimizing Free-Energy

Neural Network to Control Output of Hidden Node According to Input Patterns

ECE521 W17 Tutorial 1. Renjie Liao & Min Bai

Economic Performance Competitor Benchmarking using Data-Mining Techniques

Learning Vector Quantization (LVQ)

Biologically Inspired Compu4ng: Neural Computa4on. Lecture 5. Patricia A. Vargas

Multivariate class labeling in Robust Soft LVQ

Notes on Machine Learning for and

Learning Vector Quantization

Long-Term Time Series Forecasting Using Self-Organizing Maps: the Double Vector Quantization Method

Artificial Neural Networks Examination, March 2004

A Neural Network learning Relative Distances

Ângelo Cardoso 27 May, Symbolic and Sub-Symbolic Learning Course Instituto Superior Técnico

Classifier Conditional Posterior Probabilities

When Dictionary Learning Meets Classification

Geometric View of Machine Learning Nearest Neighbor Classification. Slides adapted from Prof. Carpuat

Counter-propagation neural networks in Matlab

Introduction to Spatial Analysis. Spatial Analysis. Session organization. Learning objectives. Module organization. GIS and spatial analysis

Frequency analysis of heavy rainfall related to synoptic weather patterns in Kyushu Island, Japan by using the Self-Organizing Map (SOM)

Empirical Risk Minimization, Model Selection, and Model Assessment

Human Mobility Pattern Prediction Algorithm using Mobile Device Location and Time Data

Forecasting Regional Employment in Germany: A Review of Neural Network Approaches. Objectives:

Weight Quantization for Multi-layer Perceptrons Using Soft Weight Sharing

Supporting Information

Artificial Neural Networks. Edward Gatt

SPATIAL-TEMPORAL TECHNIQUES FOR PREDICTION AND COMPRESSION OF SOIL FERTILITY DATA

Radial Basis Function Networks. Ravi Kaushik Project 1 CSC Neural Networks and Pattern Recognition

Least Absolute Shrinkage is Equivalent to Quadratic Penalization

Single-Trial Neural Correlates. of Arm Movement Preparation. Neuron, Volume 71. Supplemental Information

The Application of Artificial Neural Network for Forecasting Dam Spillage Events.

DIMENSION REDUCTION AND CLUSTER ANALYSIS

MAPPING OF STOCK EXCHANGES: AN ALTERNATIVE APPROACH

Hopfield Network Recurrent Netorks

ECE521 Lectures 9 Fully Connected Neural Networks

COS 429: COMPUTER VISON Face Recognition

Neural Networks for Two-Group Classification Problems with Monotonicity Hints

COMPUTATIONAL INTELLIGENCE (INTRODUCTION TO MACHINE LEARNING) SS16

Exam Machine Learning for the Quantified Self with answers :00-14:45

Ensemble determination using the TOPSIS decision support system in multi-objective evolutionary neural network classifiers

String averages and self-organizing maps for strings

Imaging of built-in electric field at a p-n junction by scanning transmission electron microscopy

Workshop Research Methods and Statistical Analysis

Quantifying Weather Risk Analysis

Appendix A.1 Derivation of Nesterov s Accelerated Gradient as a Momentum Method

CSC321 Lecture 20: Autoencoders

Dynamic Classification of Power Systems via Kohonen Neural Network

Research Methodology: Tools

Lecture 4: Statistical Hypothesis Testing

Comparison of Modern Stochastic Optimization Algorithms

Support vector machines Lecture 4

Linear classifiers Lecture 3

Introduction to Bayesian Learning. Machine Learning Fall 2018

Chapter 2 Class Notes Sample & Population Descriptions Classifying variables

Fuzzy Geographically Weighted Clustering

Contents Lecture 4. Lecture 4 Linear Discriminant Analysis. Summary of Lecture 3 (II/II) Summary of Lecture 3 (I/II)

Hidden Markov Models Part 1: Introduction

MIDTERM: CS 6375 INSTRUCTOR: VIBHAV GOGATE October,

Self-organized criticality and the self-organizing map

Data Mining Classification: Basic Concepts and Techniques. Lecture Notes for Chapter 3. Introduction to Data Mining, 2nd Edition

Cheng Soon Ong & Christian Walder. Canberra February June 2018

This is a repository copy of Improving the associative rule chaining architecture.

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

The Expectation-Maximization Algorithm

Estimation of Inelastic Response Spectra Using Artificial Neural Networks

Lecture 15. Probabilistic Models on Graph

The Spike Response Model: A Framework to Predict Neuronal Spike Trains

Homework 3 COMS 4705 Fall 2017 Prof. Kathleen McKeown

THE frequency sensitive competitive learning (FSCL) is

Fundamentals to Biostatistics. Prof. Chandan Chakraborty Associate Professor School of Medical Science & Technology IIT Kharagpur

Hypothesis Evaluation

Unsupervised learning: beyond simple clustering and PCA

Machine Learning Linear Classification. Prof. Matteo Matteucci

Manual for a computer class in ML

Spatial Analysis I. Spatial data analysis Spatial analysis and inference

Performance Comparison of K-Means and Expectation Maximization with Gaussian Mixture Models for Clustering EE6540 Final Project

Advanced Machine Learning Practical 4b Solution: Regression (BLR, GPR & Gradient Boosting)

Machine Learning. Probabilistic KNN.

Transcription:

Supplemental file 1: SOM training parameters and sensitivity analysis. Table S1 shows the SOM parameters that were used in the main manuscript. These correspond to the default set of options of the SOM Toolbox. Table S1: Default SOM training parameters. SOM parameter Option that was used Normalization Range scaling to a [-1 1] interval Shape 2D sheet Lattice Hexagonal Map size Default option: 9 x 7 Initialization Linear Training algorithm Batch training Learning function Reciprocally decreasing Neighborhood function Gaussian Training length Rough training 5 iterations Fine tuning 17 iterations Neighborhood radius Rough training 2 (initial) 1 (final) Fine tuning 1 (initial) 1 (final) The three measures of SOM quality for this default set of options are given below (definitions from Kaski and Lagus 1996; Vesanto et al. 2000): - Quantization error (QE): average Euclidean distance between normalized input vectors and their best-matching unit (after the training process): 0.651. - Topographical error (TE): percentage of input vectors for which the best-matching unit and second best-matching units are not neighbours: 0.026 (i.e. 4 trials). - Combined error (CE): average Euclidean distance between an input vector and their second best-matching unit, passing first through the best-matching unit and then through the shortest path of neighbouring units towards the second best-matching unit: 0.917. A quality and sensitivity analysis of the SOM methodology was done to assess the robustness of the analysis performed in the main manuscript. We performed 1728 simulations on the dataset with different choices for the main training parameters (normalization, shape, lattice, map size, initialization, training length, training algorithm and neighborhood function). For each simulation, we extracted the SOM quality measures and the results of the Stuart-Maxwell test of the pre-post contingency table of cluster membership. Results showed that the default set of options lay at the 2.34, 1.35 and 9.02 percentiles of QE, TE and CE respectively. Figures S1-S3 show differences between the options of training parameters in the map quality. However, the conclusions drawn from the hypothesis tests were not largely affected by the choice of training parameters. All simulations showed a p < 0.05 for the Stuart-Maxwell test on the slackline while only 4 simulations (0.23%) showed a p < 0.05 for the flamingo test. These results demonstrate the strong robustness of the conclusions with respect to the choice of training parameters.

Fig S1: Topological error for all simulations, shown in boxplots per option of each training parameter. Fig S2: Quantization error for all simulations, shown in boxplots per option of each training parameter. Fig S3: Combined error for all simulations, shown in boxplots per option of each training parameter.

References Kaski S, Lagus K (1996) Comparing self-organizing maps. In: von der Malsburg W, von Seelen JC, Sendhoff B (eds) Proceedings of ICANN96, International Conference in Artificial Neural Networks, Lecture Notes in Computer Science. Springer, Berlin, pp 809 814 Vesanto J, Himberg J, Alhoniemi E, Parhankangas J (2000) SOM Toolbox for Matlab 5.

Supplemental file 2: Table showing the mean ± SD of all 45 variables per condition. Variables Flamingo Slackline Pre test Post test Pre test Post test RANGE of MOTION Ankle pro/supination ( ) 17,12 ± 6,54 16,09 ± 4,67 12,77 ± 5,91 16,02 ± 5,75 Ankle plantar/dorsal flexion ( ) 5,55 ± 2,17 6,28 ± 2,34 5,52 ± 2,81 6,50 ± 2,42 Knee flexion/extension ( ) 11,10 ± 5,39 11,72 ± 5,55 12,46 ± 6,24 13,05 ± 5,48 Hip flexion/extension ( ) 17,34 ± 11,43 21,09 ± 12,16 14,96 ± 10,37 20,84 ± 10,75 Hip ab/adduction ( ) 24,25 ± 16,52 24,81 ± 12,01 24,43 ± 12,54 30,19 ± 15,15 Pelvis rotation ( ) 18,01 ± 10,55 19,05 ± 9,60 16,31 ± 7,00 16,81 ± 7,01 Pelvis lateral tilt ( ) 27,46 ± 17,22 28,64 ± 13,72 24,15 ± 10,58 31,57 ± 17,07 Pelvis sagital tilt ( ) 9,75 ± 8,33 12,39 ± 7,64 9,74 ± 9,01 12,33 ± 7,43 Trunk rotation ( ) 21,59 ± 10,78 25,54 ± 10,01 28,97 ± 12,99 31,69 ± 18,40 Trunk lateral tilt ( ) 50,90 ± 25,32 52,93 ± 22,16 55,59 ± 16,54 60,39 ± 23,23 Trunk sagital tilt ( ) 16,00 ± 11,70 21,85 ± 19,22 19,00 ± 17,71 23,69 ± 17,07 CoM ant-post (cm) 4,05 ± 1,98 3,78 ± 1,66 3,96 ± 2,12 4,95 ± 1,96 CoM left-right (cm) 4,84 ± 1,72 5,72 ± 2,86 5,96 ± 2,62 5,52 ± 2,42 CoM vertical (cm) 5,58 ± 3,59 7,24 ± 4,46 5,11 ± 3,26 7,25 ± 3,86 VELOCITIES and ACCELERATIONS Ankle pro/supination ( /s) 11,40 ± 5,58 11,49 ± 4,41 11,06 ± 4,56 10,37 ± 3,93 Ankle plantar/dorsal flexion ( /s) 3,40 ± 1,17 3,78 ± 1,11 4,97 ± 1,66 4,05 ± 1,08 Knee flexion/extension ( /s) 7,98 ± 3,44 7,69 ± 2,80 11,01 ± 4,65 7,79 ± 2,77 Hip flexion/extension ( /s) 8,22 ± 3,33 8,05 ± 3,95 12,80 ± 5,48 7,96 ± 3,57 Hip ab/adduction ( /s) 416,73 ± 598,87 226,21 ± 335,19 463,27 ± 702,69 309,60 ± 45312, Pelvis rotation ( /s) 8,48 ± 2,85 8,40 ± 3,56 15,78 ± 7,71 10,06 ± 7,26 Pelvis lateral tilt ( /s) 10,56 ± 6,22 10,15 ± 6,40 15,25 ± 5,11 10,49 ± 5,21 Pelvis sagital tilt ( /s) 4,25 ± 1,92 4,46 ± 2,19 6,97 ± 3,68 4,71 ± 2,23 Trunk rotation ( /s) 8,67 ± 4,12 9,93 ± 4,26 18,27 ± 9,13 10,28 ± 5,88 Trunk lateral tilt ( /s) 20,48 ± 9,56 18,18 ± 8,26 29,13 ± 8,06 18,86 ± 6,87 Trunk sagital tilt ( /s) 5,48 ± 3,27 4,83 ± 2,13 8,23 ± 6,33 5,03 ± 2,99 CoM ant-post (m/s) 1,39 ± 0,51 1,39 ± 0,57 2,07 ± 1,04 1,42 ± 0,49 CoM left-right (m/s) 1,65 ± 0,70 1,55 ± 0,57 3,15 ± 1,13 1,77 ± 0,72 CoM vertical (m/s) 2,16 ± 1,02 1,99 ± 0,93 3,52 ± 2,79 2,26 ± 0,89 Head ant-post (m/s²) 18,05 ± 6,80 17,03 ± 5,06 29,59 ± 11,40 18,27 ± 6,38 Head left-right (m/s²) 53,13 ± 23,26 48,87 ± 14,91 90,10 ± 37,09 53,04 ± 27,67 Head vertical (m/s²) 30,11 ± 15,16 28,65 ± 9,66 51,92 ± 22,21 31,85 ± 13,27 FREQUENCIES Ankle pro/supination (Hz) 1,18 ± 0,58 0,96 ± 0,65 1,55 ± 1,08 0,93 ± 0,90 Ankle plantar/dorsal flexion (Hz) 0,95 ± 0,44 0,91 ± 0,56 1,76 ± 1,31 0,99 ± 0,88 Knee flexion/extension (Hz) 1,03 ± 0,48 1,03 ± 0,61 1,91 ± 1,75 0,86 ± 0,69 Hip flexion/extension (Hz) 0,85 ± 0,42 0,69 ± 0,40 2,00 ± 1,88 0,65 ± 0,88 Hip ab/adduction (Hz) 0,63 ± 0,22 0,68 ± 0,50 1,77 ± 1,97 0,60 ± 0,89 Pelvis rotation (Hz) 0,90 ± 0,61 0,79 ± 0,43 1,71 ± 1,43 0,85 ± 0,88 Pelvis lateral tilt (Hz) 0,61 ± 0,21 0,64 ± 0,42 1,31 ± 1,13 0,62 ± 0,89 Pelvis sagital tilt (Hz) 0,78 ± 0,47 0,68 ± 0,42 1,90 ± 1,85 0,84 ± 1,34 Trunk rotation (Hz) 0,81 ± 0,41 0,68 ± 0,38 1,18 ± 0,84 0,64 ± 0,45 Trunk lateral tilt (Hz) 0,56 ± 0,18 0,60 ± 0,43 1,08 ± 0,82 0,46 ± 0,43 Trunk sagital tilt (Hz) 0,60 ± 0,23 0,68 ± 0,55 1,21 ± 0,80 0,67 ± 0,88 CoM ant-post (Hz) 0,62 ± 0,20 0,64 ± 0,45 1,25 ± 1,27 0,59 ± 0,90 CoM left-right (Hz) 0,47 ± 0,21 0,45 ± 0,42 1,11 ± 0,91 0,43 ± 0,91 CoM vertical (Hz) 0,77 ± 0,25 0,61 ± 0,28 1,14 ± 0,53 0,64 ± 0,44

Supplemental file 3: SOM visualization showing the labels of all trials Labels: Subject initials FL/SL pre/post. Thick black lines show the cluster borders. Numbers give the cluster number as refered to in the main manuscript. Example illustrating the count data: subject SA. For the slackline task, she has three trials that were mapped into cluster 3 at the pre-test, while on the post-test, she has two trials in cluster 4 and one trial in cluster 2. In the slackline contingency table, she thus contributed 3 counts to the cell c32 and 6 counts to the cell c34. 2 1 4 5 3