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Other publications by Jean-Christophe Nebel

Temporal Extension of Laplacian Eigenmaps for Unsupervised Dimensionality Reduction of Time Series

M. Lewandowski, J. Martinez del Rincon, D. Makris and J.-C. Nebel

International Conference on Pattern Recognition (ICPR 2010)
2010
[PDF]

Abstract
A novel non-linear dimensionality reduction method, called Temporal Laplacian Eigenmaps, is introduced to process efficiently time series data. In this embedded-based approach, temporal information is intrinsic to the objective function, which produces description of low dimensional spaces with time coherence between data points. Since the proposed scheme also includes bidirectional mapping between data and embedded spaces and automatic tuning of key parameters, it offers the same benefits as mapping-based approaches. Experiments on a couple of computer vision applications demonstrate the superiority of the new approach to other dimensionality reduction method in term of accuracy. Moreover, its lower computational cost and generalisation abilities suggest it is scalable to larger datasets.

Cited by ( Google Scholar: 60 ISI Web of Knowledge: / & SCOPUS: 37 ): 60

2024

Activity Recognition Method Based on Kernel Supervised Laplacian Eigenmaps, P Tu, C Tian, D Du, J Li, H Wang, International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024), 2024 (G)

2023

Machine learning for administrative health records: A systematic review of techniques and applications, Adrian Caruana, Madhushi Bandara, Katarzyna Musial, Daniel Catchpoole, Paul J. Kennedy, Artificial Intelligence in Medicine, 2023 (G)
A Survey on Dimensionality Reduction Techniques for Time-series Data, Mohsena Ashraf, Farzana Anowar, Jahanggir H. Setu, Atiqul I. Chowdhury, Eshtiak Ahmed, Ashraful Islam, Abdullah Al-Mamun, IEEE Access PP(99):1-1, DOI: 10.1109/ACCESS.2023.3269693, 2023 (G)
Data Preprocessing Technology in Pipeline Health Monitoring, Lu, H., Xu, ZD., Iseley, T., Peng, H., Fu, L., In: Pipeline Inspection and Health Monitoring Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-6798-6_5, 2023 (G)

2022

Longitudinal clustering analysis and prediction of Parkinson’s disease progression using radiomics and hybrid machine learning, Salmanpour M.R., Shamsaei M., Hajianfar G., Soltanian-Zadeh H., Rahmim A., Quantitative Imaging in Medicine and Surgery, 12(2), pp. 906-919, 2022 (G) (S)
Explainable Classification of Uncertain Time Series, Michael Franklin Mbouopda, PhD Thesis, Universite Clermont Auvergne (France), 2022 (G)

2021

Robust identification of Parkinson's disease subtypes using radiomics and hybrid machine learning, Mohammad R.Salmanpour, Mojtaba Shamsaei, Abdollah Saberi, Ghasem Hajianfar, Hamid Soltanian-Zadeh, Arman Rahmim, Computers in Biology and Medicine, Volume 129, February 2021, 104142 (G)

2020

Diagnosis Approaches for Colorectal Cancer Using Manifold Learning and Deep Learning, Thanh-Hai, N., Thai-Nghe, N., SN COMPUT. SCI. 1, 281, https://doi.org/10.1007/s42979-020-00297-7, 2020 (G)
Continuous statistical modelling in characterisation of complex hydrocolloid mixtures using near infrared spectroscopy, Konstantia Georgouli, Beatriz Carrasco, Damien Vincke, Jesus Martinez Del Rincon, Anastasios Koidis, Vincent Baeten, Juan Antonio Fernandez Pierna, Chemometrics and Intelligent Laboratory Systems, Volume 196, 2020 (G) (S)

2018

Refining the Pose: Training and Use of Deep Recurrent Autoencoders for Improving Human Pose Estimation, Niall McLaughlin, Jesus Martinez-del-Rincon, In book: Articulated Motion and Deformable Objects, Lecture Notes in Computer Science, 10945 LNCS, pp. 11-23, DOI: 10.1007/978-3-319-94544-6_2, 2018 (G) (S)
Improvements in LTE-Advanced Time Series Prediction with Dimensionality Reduction Algorithms, Alexandra Mercader ; Jonathan Ah Sue ; Ralph Hasholzner ; Johannes Brendel, 2018 IEEE 5G World Forum (5GWF), 2018 (G) (S)

2017

Modelling the Dynamics of Biological Systems with the Geometric Hidden Markov Model, B Vangelov, M Barahona - bioRxiv, 2017, doi: https://doi.org/10.1101/224063 (G)
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series, Hamid Dadkhahi, Marco F. Duarte, Benjamin Marlin, IEEE Transactions on Image Processing, 2017 (G) (S)
Automated Analysis and Quantification of Human Mobility using a Depth Sensor, Dan Leightley, Jamie S. McPhee, Moi Hoon Yap, IEEE Journal of Biomedical and Health Informatics, 21(4): 939-948, 2017 (G) (S)
Linear Latent Low Dimensional Space for Online Early Action Recognition and Prediction, V Bloom, V Argyriou, D Makris, Pattern Recognition, 2017 (G) (S)
Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data, K Georgouli, JM Del Rincon, A Koidis, 217, p. 735-742, Food Chemistry, 2017 (G) (S)

2016

Group-constrained manifold learning: Application to AD risk assessment, R. Guerrero, C. Ledig, A. Schmidt-Richberg, and D. Rueckert, Pattern Recognition, in press, 2016 (G)
Learning representations from multiple manifolds, Chan-Su Lee, Ahmed Elgammal, Marwan Torki, Pattern Recognition, Volume 50, pp. 74-87, 2016 (G) (S)
Unsupervised Anomaly Detection in Sequences Using Long Short Term Memory Recurrent Neural Networks, Majid Aldosari, MSc Thesis, George Mason University, 2016
Learning from Pairwise Proximity Data, Hamid Dadkhahi, PhD Thesis, University of Massachusetts Amherst, 2016 (G)

2015

Hierarchical Transfer Learning for Online Recognition of Compound Actions, Victoria Bloom, Vasileios Argyriou, Dimitrios Makris, Computer Vision and Image Understanding, Available online 11 December 2015 (G) (S)
Modeling Time Series and Sequences: Learning Representations and Making Predictions, Wenzhao Lian, PhD Thesis, Department of Electrical and Computer Engineering, Duke University, 2015 (G)
Group-Constrained Laplacian Eigenmaps: Longitudinal AD Biomarker Learning, R. Guerrero, C. Ledig, A. Schmidt-Richberg, and D. Rueckert, L. Zhou et al. (Eds.): Machine Learning in Medical Imaging (MLMI 2015), LNCS 9352, pp. 178-185, 2015 (G) (S)
View-invariant human action recognition via robust locally adaptive multi-view learning, Jia-geng FENG, Jun XIAO, Frontiers of Information Technology & Electronic Engineering, DOI: 10.1631/FITEE.1500080, 2015 (G) (S)
Isomap Out-of-Sample Extension for Noisy Time Series Data, Hamid Dadkhahi, Marco F. Duarte, Benjamin Marlin, IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Boston, MA., USA, 2015 (G) (S)
Silhouette Analysis for Human Action Recognition Based on Supervised Temporal t-SNE and Incremental Learning, Cheng, J. ; Liu, H. ; Wang, F. ; Li, H., IEEE Transactions on Image Processing, Volume:PP, Issue: 99, 2015 (G) (S)
Multivariate Time-Series Analysis and Diffusion Maps, W Lian, R Talmon, H Zaveri, L Carin, R Coifman, Signal Processing, 2015 (G) (S)
Efficient tracking of human poses using a manifold hierarchy, A. Moutzouris, J. Martinez del Rincon, J.-C. Nebel and D. Makris, Computer Vision and Image Understanding, 132: 75-86, 2015 (G) (S)
3D human action recognition and motion analysis using selective representations, Daniel Leightley, PhD Thesis, Manchester Metropolitan University, 2015 (G)
Multiple Action Recognition for Video Games (MARViG), Victoria Bloom, PhD Thesis, Kingston University, 2015 (G)
Fuzzy qualitative approach to address uncertainty in human motion analysis, Chern Hong Lim, PhD thesis, University of Malaya, 2015 (G)
Unravelling biological processes using graph theoretical algorithms and probabilistic models, Borislav Vangelov, PhD Thesis, Mathematics, Imperial College London, 2015 (G)

2014

Clustered Spatio-Temporal Manifolds for Online Action Recognition, Victoria Bloom, Dimitrios Makris, Vasileios Argyriou, International Conference on Pattern Recognition (ICPR), 2014 (G) (S)
Automatic Segmentation and Recognition of Human Actions in Monocular Sequences, Carlos Orrite, Mario Rodriguez, Elias Herrero, Gregory Rogez, Sergio A. Velastin, International Conference on Pattern Recognition (ICPR), 2014 (G) (S)
Probability Ridges and Distortion Flows: Visualizing Multivariate Time Series Using a Variational Bayesian Manifold Learning Method, A Tosi, I Olier, A Vellido - Advances in Self-Organizing Maps and Learning Vector Quantization - Advances in Intelligent Systems and Computing, Volume 295, pp 55-64, 2014 (G) (S)
Structural Laplacian Eigenmaps for Modeling Sets of Multivariate Sequences, M Lewandowski, D. Makris, S.A. Velastin and J.-C. Nebel, IEEE Transactions on Cybernetics, 44(6): 936-949, 2014 (G) (S)
Human Action Recognition Using Maximum Temporal Inter-Class Dissimilarity, Haijun Liu, Lan Li, Proceedings of the Second International Conference on Communications, Signal Processing, and Systems, Lecture Notes in Electrical Engineering Volume 246, pp 961-969, 2014 (G) (S)
Generalised Laplacian Eigenmaps for Modelling and Tracking Human Motions, J. Martinez del Rincon, M. Lewandowski, J.-C. Nebel and Dimitrios Makris, IEEE Transactions on Cybernetics, in press, 2014 (G) (S)
Visualization and interpretability in probabilistic dimensionality reduction models, Alessandra Tosi, PhD Thesis, Department of Software, Universitat Politecnica de Catalunya, Spain, 2014 (G)

2013

A Nonlinear Semantic-Preserving Projection Approach to Visualize Multivariate Periodical Time Series, Pierre Blanchart and Marine Depecker, IEEE Transactions on Neural Networks and Learning Systems, 2013 (G) (S)
Silhouette analysis for human action recognition based on maximum spatio-temporal dissimilarity embedding, Jian Cheng, Haijun Liu, Hongsheng Li, Machine Vision and Applications, 2013 (G) (S)
OBST-based segmentation approach to financial time series, Yain-Whar Si, Jiangling Yin, Engineering Applications of Artificial Intelligence, 2013 (G) (S)
Dependency Modeling for Information Fusion with Applications in Visual Recognition, MA Jinhua, PhD Thesis, Hong Kong Baptist University, August 2013 (G)
Contextual analysis of videos Thida, Thida M., Eng H.-L., Monekosso D., Remagnino P., Synthesis Lectures on Image, Video and Multimedia Processing, Volume 14, pp 1-104, 2013 (S)
Action Recognition Based on Motion Representing and Reconstructed Phase Spaces Matching of 3D Joint Positions, Yan Tao Zhao, Bo Zhang, Xu Guang Zhang, Xiao Li Li, Mei Ling Fu, Si Yuan Feng, Applied Mechanics and Materials, 333-335, 675, 2013 (G) (S)
Gene Expression Data Classification Using Laplacian Eigenmap Based on Improved Maximum Margin Criterion N WANG, J WANG, F GE - Chinese Journal of Electronics, 2013 (G) (S)
Supervised Spatio-Temporal Neighborhood Topology Learning for Action Recognition, A. Ma, IEEE Transactions on Circuits and Systems for Video Technology, 99, 2013 (G) (S)
Video analysis based on Multi-Kernel Representation with automatic parameter choice, A.M. Alvarez-Meza, J. Valencia-Aguirre, G. Daza-Santacoloma, C.D. Acosta-Medina, G. Castellanos-Dominguez, Neurocomputing, Volume 100, 16 January 2013, Pages 117-126, 2013 (G) (S)

2012

Human Pose Tracking by Hierarchical Manifold Searching, Alexandros Moutzouris, Jesus Martinez del Rincon, Jean-Christophe Nebel, Dimitrios Makris, International Conference on Pattern Recognition (ICPR 2012), November 11-15, Tsukuba, Japan, 2012 (G) (S)
Model-based recognition of human actions by trajectory matching in phase spaces, Adolfo Lopez-Mendez, Josep R. Casas, Image and Vision Computing, Volume 30, Issue 11, November 2012, Pages 808-816, 2012 (G) (S)
Bag of Delay-Vectors for human action recognition, Andreu Villagrasa, Aitor David, Master thesis, Universitat Politecnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, oct, 2012 (G)
Action recognition in visual sensor networks: a data fusion perspective Cilla Ugarte, Rodrigo, PhD Thesis, Universidad Carlos III de Madrid, Departamento de Informatica, Nov-2012 (G)

2011

Two-Manifold Problems, Byron Boots, Geoffrey J. Gordon, arXiv:1112.6399, 2011 (G)
Nonlinear Dimensionality Reduction Frameworks to Support Machine Learning Systems, Andres Marino Alvarez Meza, MSc Thesis, Master in Engineering - Industrial Automation, Universidad Nacional de Colombia, 2011 (G)
Are Current Monocular Computer Vision Systems for Human Action Recognition Suitable for Visual Surveillance Applications?, J.-C. Nebel, M. Lewandowski, J. Thevenon, F. Martinez and S. Velastin, International Symposium on Visual Computing, September 26-28, Las Vegas, Nevada, USA, 2011 (G) (S)
Human pose tracking in low dimensional space enhanced by limb correction, A. Moutzouris, J Martinez del Rincon, M Lewandowski, J.-C. Nebel and D. Makris, IEEE International Conference on Image Processing, Sept 11, Brussels, 2011 (G) (S)
Graph-based Particle Filter for Human Tracking with Stylistic Variations, J Martinez del Rincon, J.-C. Nebel and D. Makris, British Machine Vision Conference (BMVC 2011), September, Dundee, UK, 2011 (G) (S)
Probabilistic Feature Extraction from Time Series using Spatio-Temporal Constraints, M. Lewandowski, D. Makris and J.-C. Nebel, Pacific-Asia Conference on Knowledge Discovery and Data Mining, May 24-27, Shenzhen, China, 2011 (G) (S)
Manifold learning for visual data analysis, Yang Liu, PhD Thesis, Hong Kong Polytechnic University, 2011 (G)

2010

View and Style-Independent Action Manifolds for Human Activity Recognition, M. Lewandowski, D. Makris and J.-C. Nebel, European Conference on Computer Vision (ECCV 2010), September 5-11, Crete, Greece, 2010 (G) (S)

j.nebel@kingston.ac.uk