The most recent list can be found on on google scholar.
Shnitzer, Tal; Lederman, Roy R; Liu, Gi-Ren; Talmon, Ronen; Wu, Hau-Tieng
Diffusion operators for multimodal data analysis Incollection
In: Handbook of Numerical Analysis, vol. 20, pp. 1–39, Elsevier, 2019, ISBN: 978-0-444-64140-3.
Links | BibTeX | Tags: Alternating Diffusion, BookChapter, Common variable, diffusion maps, Manifold Learning, Multi-view, multimodal, Multimodal data, Sensor fusion, Shape differences
@incollection{shnitzer_diffusion_2019,
title = {Diffusion operators for multimodal data analysis},
author = {Tal Shnitzer and Roy R Lederman and Gi-Ren Liu and Ronen Talmon and Hau-Tieng Wu},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1570865919300213},
doi = {10.1016/bs.hna.2019.07.008},
isbn = {978-0-444-64140-3},
year = {2019},
date = {2019-01-01},
urldate = {2020-08-13},
booktitle = {Handbook of Numerical Analysis},
volume = {20},
pages = {1--39},
publisher = {Elsevier},
keywords = {Alternating Diffusion, BookChapter, Common variable, diffusion maps, Manifold Learning, Multi-view, multimodal, Multimodal data, Sensor fusion, Shape differences},
pubstate = {published},
tppubtype = {incollection}
}
Lederman, Roy R; Talmon, Ronen
Learning the geometry of common latent variables using alternating-diffusion Journal Article
In: Applied and Computational Harmonic Analysis, vol. 44, no. 3, pp. 509–536, 2018, ISSN: 1063-5203.
Abstract | Links | BibTeX | Tags: Algorithms, Alternating Diffusion, Alternating-diffusion, Common variable, diffusion maps, Diffusion-maps, Multi-view, multimodal, Multimodal analysis
@article{lederman_learning_2018,
title = {Learning the geometry of common latent variables using alternating-diffusion},
author = {Roy R Lederman and Ronen Talmon},
url = {http://www.sciencedirect.com/science/article/pii/S1063520315001190},
doi = {10.1016/j.acha.2015.09.002},
issn = {1063-5203},
year = {2018},
date = {2018-01-01},
urldate = {2020-08-13},
journal = {Applied and Computational Harmonic Analysis},
volume = {44},
number = {3},
pages = {509--536},
abstract = {One of the challenges in data analysis is to distinguish between different sources of variability manifested in data. In this paper, we consider the case of multiple sensors measuring the same physical phenomenon, such that the properties of the physical phenomenon are manifested as a hidden common source of variability (which we would like to extract), while each sensor has its own sensor-specific effects (hidden variables which we would like to suppress); the relations between the measurements and the hidden variables are unknown. We present a data-driven method based on alternating products of diffusion operators and show that it extracts the common source of variability. Moreover, we show that it extracts the common source of variability in a multi-sensor experiment as if it were a standard manifold learning algorithm used to analyze a simple single-sensor experiment, in which the common source of variability is the only source of variability.},
keywords = {Algorithms, Alternating Diffusion, Alternating-diffusion, Common variable, diffusion maps, Diffusion-maps, Multi-view, multimodal, Multimodal analysis},
pubstate = {published},
tppubtype = {article}
}
Lederman, Roy R; Talmon, Ronen; Wu, Hau-tieng; Lo, Yu-Lun; Coifman, Ronald R
Alternating diffusion for common manifold learning with application to sleep stage assessment Inproceedings
In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5758–5762, 2015, (ISSN: 2379-190X).
Abstract | Links | BibTeX | Tags: Alternating Diffusion, Common variable, diffusion maps, Kernel, learning (artificial intelligence), Manifolds, multimodal, multimodal respiratory signals, multimodal signal processing, Physiology, Sensitivity, Sensor phenomena and characterization, signal processing, sleep, sleep stage assessment, standard manifold learning method, time series
@inproceedings{lederman_alternating_2015,
title = {Alternating diffusion for common manifold learning with application to sleep stage assessment},
author = {Roy R Lederman and Ronen Talmon and Hau-tieng Wu and Yu-Lun Lo and Ronald R Coifman},
doi = {10.1109/ICASSP.2015.7179075},
year = {2015},
date = {2015-01-01},
booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {5758--5762},
abstract = {In this paper, we address the problem of multimodal signal processing and present a manifold learning method to extract the common source of variability from multiple measurements. This method is based on alternating-diffusion and is particularly adapted to time series. We show that the common source of variability is extracted from multiple sensors as if it were the only source of variability, extracted by a standard manifold learning method from a single sensor, without the influence of the sensor-specific variables. In addition, we present application to sleep stage assessment. We demonstrate that, indeed, through alternating-diffusion, the sleep information hidden inside multimodal respiratory signals can be better captured compared to single-modal methods.},
note = {ISSN: 2379-190X},
keywords = {Alternating Diffusion, Common variable, diffusion maps, Kernel, learning (artificial intelligence), Manifolds, multimodal, multimodal respiratory signals, multimodal signal processing, Physiology, Sensitivity, Sensor phenomena and characterization, signal processing, sleep, sleep stage assessment, standard manifold learning method, time series},
pubstate = {published},
tppubtype = {inproceedings}
}
Lederman, Roy R; Talmon, Ronen
Common Manifold Learning Using Alternating-Diffusion Technical Report
Yale CS no. YALEU/DCS/TR-1497, 2014.
Links | BibTeX | Tags: AD, Algorithms, Alternating Diffusion, Manifold Learning
@techreport{lederman_common_2014,
title = {Common Manifold Learning Using Alternating-Diffusion},
author = {Roy R Lederman and Ronen Talmon},
url = {https://cpsc.yale.edu/sites/default/files/files/tr1497.pdf},
year = {2014},
date = {2014-01-01},
number = {YALEU/DCS/TR-1497},
pages = {42},
institution = {Yale CS},
keywords = {AD, Algorithms, Alternating Diffusion, Manifold Learning},
pubstate = {published},
tppubtype = {techreport}
}