The most recent list can be found on on google scholar.
Katz, Ori; Lederman, Roy R; Talmon, Ronen
Spectral Flow on the Manifold of SPD Matrices for Multimodal Data Processing Technical Report
2020, (arXiv: 2009.08062).
Abstract | Links | BibTeX | Tags: Common variable, Computer Science - Machine Learning, Manifold Learning, Multi-view, multimodal, SPD Matrices, Statistics - Machine Learning
@techreport{katz_spectral_2020,
title = {Spectral Flow on the Manifold of SPD Matrices for Multimodal Data Processing},
author = {Ori Katz and Roy R Lederman and Ronen Talmon},
url = {http://arxiv.org/abs/2009.08062},
year = {2020},
date = {2020-09-01},
urldate = {2020-11-25},
abstract = {In this paper, we consider data acquired by multimodal sensors capturing complementary aspects and features of a measured phenomenon. We focus on a scenario in which the measurements share mutual sources of variability but might also be contaminated by other measurement-specific sources such as interferences or noise. Our approach combines manifold learning, which is a class of nonlinear data-driven dimension reduction methods, with the well-known Riemannian geometry of symmetric and positive-definite (SPD) matrices. Manifold learning typically includes the spectral analysis of a kernel built from the measurements. Here, we take a different approach, utilizing the Riemannian geometry of the kernels. In particular, we study the way the spectrum of the kernels changes along geodesic paths on the manifold of SPD matrices. We show that this change enables us, in a purely unsupervised manner, to derive a compact, yet informative, description of the relations between the measurements, in terms of their underlying components. Based on this result, we present new algorithms for extracting the common latent components and for identifying common and measurement-specific components.},
note = {arXiv: 2009.08062},
keywords = {Common variable, Computer Science - Machine Learning, Manifold Learning, Multi-view, multimodal, SPD Matrices, Statistics - Machine Learning},
pubstate = {published},
tppubtype = {techreport}
}
In this paper, we consider data acquired by multimodal sensors capturing complementary aspects and features of a measured phenomenon. We focus on a scenario in which the measurements share mutual sources of variability but might also be contaminated by other measurement-specific sources such as interferences or noise. Our approach combines manifold learning, which is a class of nonlinear data-driven dimension reduction methods, with the well-known Riemannian geometry of symmetric and positive-definite (SPD) matrices. Manifold learning typically includes the spectral analysis of a kernel built from the measurements. Here, we take a different approach, utilizing the Riemannian geometry of the kernels. In particular, we study the way the spectrum of the kernels changes along geodesic paths on the manifold of SPD matrices. We show that this change enables us, in a purely unsupervised manner, to derive a compact, yet informative, description of the relations between the measurements, in terms of their underlying components. Based on this result, we present new algorithms for extracting the common latent components and for identifying common and measurement-specific components.
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}
}
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.
Shaham, Uri; Lederman, Roy R
Learning by coincidence: Siamese networks and common variable learning Journal Article
In: Pattern Recognition, vol. 74, pp. 52–63, 2018, ISSN: 00313203.
Links | BibTeX | Tags: Common variable, Deep Learning, Multi-view, multimodal, Siamese networks
@article{shaham_learning_2018,
title = {Learning by coincidence: Siamese networks and common variable learning},
author = {Uri Shaham and Roy R Lederman},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0031320317303588},
doi = {10.1016/j.patcog.2017.09.015},
issn = {00313203},
year = {2018},
date = {2018-01-01},
urldate = {2020-08-13},
journal = {Pattern Recognition},
volume = {74},
pages = {52--63},
keywords = {Common variable, Deep Learning, Multi-view, multimodal, Siamese networks},
pubstate = {published},
tppubtype = {article}
}
Shaham, Uri; Lederman, Roy R
Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks Technical Report
2015.
Abstract | Links | BibTeX | Tags: Common variable, Deep Learning, Multi-view
@techreport{shaham_common_2015,
title = {Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks},
author = {Uri Shaham and Roy R Lederman},
url = {https://arxiv.org/abs/1512.08806v3},
year = {2015},
date = {2015-12-01},
urldate = {2020-08-13},
abstract = {We consider the statistical problem of learning common source of variability
in data which are synchronously captured by multiple sensors, and demonstrate
that Siamese neural networks can be naturally applied to this problem. This
approach is useful in particular in exploratory, data-driven applications,
where neither a model nor label information is available. In recent years, many
researchers have successfully applied Siamese neural networks to obtain an
embedding of data which corresponds to a "semantic similarity". We present an
interpretation of this "semantic similarity" as learning of equivalence
classes. We discuss properties of the embedding obtained by Siamese networks
and provide empirical results that demonstrate the ability of Siamese networks
to learn common variability.},
keywords = {Common variable, Deep Learning, Multi-view},
pubstate = {published},
tppubtype = {techreport}
}
We consider the statistical problem of learning common source of variability
in data which are synchronously captured by multiple sensors, and demonstrate
that Siamese neural networks can be naturally applied to this problem. This
approach is useful in particular in exploratory, data-driven applications,
where neither a model nor label information is available. In recent years, many
researchers have successfully applied Siamese neural networks to obtain an
embedding of data which corresponds to a "semantic similarity". We present an
interpretation of this "semantic similarity" as learning of equivalence
classes. We discuss properties of the embedding obtained by Siamese networks
and provide empirical results that demonstrate the ability of Siamese networks
to learn common variability.
in data which are synchronously captured by multiple sensors, and demonstrate
that Siamese neural networks can be naturally applied to this problem. This
approach is useful in particular in exploratory, data-driven applications,
where neither a model nor label information is available. In recent years, many
researchers have successfully applied Siamese neural networks to obtain an
embedding of data which corresponds to a "semantic similarity". We present an
interpretation of this "semantic similarity" as learning of equivalence
classes. We discuss properties of the embedding obtained by Siamese networks
and provide empirical results that demonstrate the ability of Siamese networks
to learn common variability.