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}
}
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
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}
}