Search Results for author: Ronen Talmon

Found 26 papers, 5 papers with code

Unsupervised Detection of Sub-Territories of the Subthalamic Nucleus During DBS Surgery with Manifold Learning

no code implementations23 Aug 2022 Ido Cohen, Dan Valsky, Ronen Talmon

Compared to a competing supervised algorithm based on a Hidden Markov Model, our unsupervised method demonstrates similar results in the STN detection task and superior results in the DLOR detection task.

ManiFeSt: Manifold-based Feature Selection for Small Data Sets

no code implementations18 Jul 2022 David Cohen, Tal Shnitzer, Yuval Kluger, Ronen Talmon

This in turn allows for the extraction of the hidden manifold underlying the features and avoids overfitting, facilitating few-sample FS.

Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators

no code implementations21 Jan 2022 Tal Shnitzer, Hau-Tieng Wu, Ronen Talmon

Our approach combines three components that are often considered separately: (i) manifold learning for building operators representing the geometry of the variables, (ii) Riemannian geometry of symmetric positive-definite matrices for multiscale composition of operators corresponding to different time samples, and (iii) spectral analysis of the composite operators for extracting different dynamic modes.

Time Series

Hyperbolic Procrustes Analysis Using Riemannian Geometry

1 code implementation NeurIPS 2021 Ya-Wei Eileen Lin, Yuval Kluger, Ronen Talmon

Here, we take a purely geometric approach for label-free alignment of hierarchical datasets and introduce hyperbolic Procrustes analysis (HPA).


Single Independent Component Recovery and Applications

no code implementations12 Oct 2021 Uri Shaham, Jonathan Svirsky, Ori Katz, Ronen Talmon

Latent variable discovery is a central problem in data analysis with a broad range of applications in applied science.

Image Generation

Time Series Forecasting Using Manifold Learning

no code implementations7 Oct 2021 Panagiotis Papaioannou, Ronen Talmon, Ioannis Kevrekidis, Constantinos Siettos

We address a three-tier numerical framework based on manifold learning for the forecasting of high-dimensional time series.


Joint Geometric and Topological Analysis of Hierarchical Datasets

no code implementations3 Apr 2021 Lior Aloni, Omer Bobrowski, Ronen Talmon

At the finer (sample) level, we devise a new metric between samples based on manifold learning that facilitates quantitative structural analysis.

Topological Data Analysis

Spectral Flow on the Manifold of SPD Matrices for Multimodal Data Processing

1 code implementation17 Sep 2020 Ori Katz, Roy R. Lederman, Ronen Talmon

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.

Dimensionality Reduction

Kernel-based parameter estimation of dynamical systems with unknown observation functions

no code implementations9 Sep 2020 Ofir Lindenbaum, Amir Sagiv, Gal Mishne, Ronen Talmon

A low-dimensional dynamical system is observed in an experiment as a high-dimensional signal; for example, a video of a chaotic pendulums system.

Symmetric Positive Semi-definite Riemannian Geometry with Application to Domain Adaptation

no code implementations28 Jul 2020 Or Yair, Almog Lahav, Ronen Talmon

In this paper, we present new results on the Riemannian geometry of symmetric positive semi-definite (SPSD) matrices.

Domain Adaptation

Unsupervised ensembling of multiple software sensors: a new approach for electrocardiogram-derived respiration using one or two channels

no code implementations23 Jun 2020 John Malik, Yu-Ting Lin, Ronen Talmon, Hau-Tieng Wu

While several electrocardiogram-derived respiratory (EDR) algorithms have been proposed to extract breathing activity from a single-channel ECG signal, conclusively identifying a superior technique is challenging.

Spectral Discovery of Jointly Smooth Features for Multimodal Data

no code implementations9 Apr 2020 Felix Dietrich, Or Yair, Rotem Mulayoff, Ronen Talmon, Ioannis G. Kevrekidis

We show analytically that our method is guaranteed to provide a set of orthogonal functions that are as jointly smooth as possible, ordered by increasing Dirichlet energy from the smoothest to the least smooth.

Option Discovery in the Absence of Rewards with Manifold Analysis

1 code implementation ICML 2020 Amitay Bar, Ronen Talmon, Ron Meir

Options have been shown to be an effective tool in reinforcement learning, facilitating improved exploration and learning.


Domain Adaptation with Optimal Transport on the Manifold of SPD matrices

no code implementations3 Jun 2019 Or Yair, Felix Dietrich, Ronen Talmon, Ioannis G. Kevrekidis

We model the difference between two domains by a diffeomorphism and use the polar factorization theorem to claim that OT is indeed optimal for DA in a well-defined sense, up to a volume preserving map.

Domain Adaptation

Intrinsic Isometric Manifold Learning with Application to Localization

no code implementations1 Jun 2018 Ariel Schwartz, Ronen Talmon

Specifically, we show that our proposed method facilitates accurate localization of a moving agent from imaging data it collects.

Indoor Localization

Parametric Manifold Learning Via Sparse Multidimensional Scaling

no code implementations ICLR 2018 Gautam Pai, Ronen Talmon, Ron Kimmel

We propose a metric-learning framework for computing distance-preserving maps that generate low-dimensional embeddings for a certain class of manifolds.

Metric Learning

DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling

no code implementations16 Nov 2017 Gautam Pai, Ronen Talmon, Alex Bronstein, Ron Kimmel

This paper explores a fully unsupervised deep learning approach for computing distance-preserving maps that generate low-dimensional embeddings for a certain class of manifolds.

Data-Driven Tree Transforms and Metrics

1 code implementation18 Aug 2017 Gal Mishne, Ronen Talmon, Israel Cohen, Ronald R. Coifman, Yuval Kluger

Often the data is such that the observations do not reside on a regular grid, and the given order of the features is arbitrary and does not convey a notion of locality.

Mahalanonbis Distance Informed by Clustering

no code implementations13 Aug 2017 Almog Lahav, Ronen Talmon, Yuval Kluger

Specifically we show that organizing similar coordinates in clusters can be exploited for the construction of the Mahalanobis distance between samples.

Diffusion-based nonlinear filtering for multimodal data fusion with application to sleep stage assessment

no code implementations13 Jan 2017 Ori Katz, Ronen Talmon, Yu-Lun Lo, Hau-Tieng Wu

We show that without prior knowledge on the different modalities and on the measured system, our method gives rise to a data-driven representation that is well correlated with the underlying sleep process and is robust to noise and sensor-specific effects.

Multimodal Latent Variable Analysis

no code implementations25 Nov 2016 Vardan Papyan, Ronen Talmon

The first step in our analysis is to find the common source of variability present in all sensor measurements.

Local Canonical Correlation Analysis for Nonlinear Common Variables Discovery

no code implementations14 Jun 2016 Or Yair, Ronen Talmon

In this paper, we address the problem of hidden common variables discovery from multimodal data sets of nonlinear high-dimensional observations.

Kernel-based Sensor Fusion with Application to Audio-Visual Voice Activity Detection

no code implementations11 Apr 2016 David Dov, Ronen Talmon, Israel Cohen

In this paper, we address the problem of multiple view data fusion in the presence of noise and interferences.

Action Detection Activity Detection

Latent common manifold learning with alternating diffusion: analysis and applications

1 code implementation30 Jan 2016 Ronen Talmon, Hau-Tieng Wu

The analysis of data sets arising from multiple sensors has drawn significant research attention over the years.

Hierarchical Coupled Geometry Analysis for Neuronal Structure and Activity Pattern Discovery

no code implementations6 Nov 2015 Gal Mishne, Ronen Talmon, Ron Meir, Jackie Schiller, Uri Dubin, Ronald R. Coifman

In the wake of recent advances in experimental methods in neuroscience, the ability to record in-vivo neuronal activity from awake animals has become feasible.

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