B
Boaz Nadler
Researcher at Weizmann Institute of Science
Publications - 157
Citations - 10038
Boaz Nadler is an academic researcher from Weizmann Institute of Science. The author has contributed to research in topics: Eigenvalues and eigenvectors & Covariance matrix. The author has an hindex of 41, co-authored 149 publications receiving 8967 citations. Previous affiliations of Boaz Nadler include Yale University & Tel Aviv University.
Papers
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Journal ArticleDOI
Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps
Ronald R. Coifman,Stephane Lafon,Ann B. Lee,Mauro Maggioni,Boaz Nadler,Frederick Warner,Steven W. Zucker +6 more
TL;DR: The process of iterating or diffusing the Markov matrix is seen as a generalization of some aspects of the Newtonian paradigm, in which local infinitesimal transitions of a system lead to global macroscopic descriptions by integration.
Journal ArticleDOI
Diffusion maps, spectral clustering and reaction coordinates of dynamical systems
TL;DR: In this paper, the authors consider a family of diffusion maps, defined as the embedding of complex data onto a low dimensional Euclidean space via the eigenvectors of suitably defined random walks defined on the given datasets.
Posted Content
Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck operators
TL;DR: In this paper, a diffusion-based probabilistic interpretation of spectral clustering and dimensionality reduction algorithms that use the eigenvectors of the normalized graph Laplacian is presented.
Proceedings Article
Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators
TL;DR: A diffusion based probabilistic interpretation of spectral clustering and dimensionality reduction algorithms that use the eigenvectors of the normalized graph Laplacian is presented.
Journal ArticleDOI
Non-Parametric Detection of the Number of Signals: Hypothesis Testing and Random Matrix Theory
Shira Kritchman,Boaz Nadler +1 more
TL;DR: This paper presents a new algorithm for detection of the number of sources via a sequence of hypothesis tests, and theoretically analyze the consistency and detection performance of the proposed algorithm, showing its superiority compared to the standard minimum description length (MDL)-based estimator.