Search Results for author: Amir Averbuch

Found 16 papers, 0 papers with code

Automated identification of transiting exoplanet candidates in NASA Transiting Exoplanets Survey Satellite (TESS) data with machine learning methods

no code implementations20 Feb 2021 Leon Ofman, Amir Averbuch, Adi Shliselberg, Idan Benaun, David Segev, Aron Rissman

A novel artificial intelligence (AI) technique that uses machine learning (ML) methodologies combines several algorithms, which were developed by ThetaRay, Inc., is applied to NASA's Transiting Exoplanets Survey Satellite (TESS) dataset to identify exoplanetary candidates.

$\ell_0$-based Sparse Canonical Correlation Analysis

no code implementations12 Oct 2020 Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger

We further propose $\ell_0$-Deep CCA for solving the problem of non-linear sparse CCA by modeling the correlated representations using deep nets.

Kernel Scaling for Manifold Learning and Classification

no code implementations4 Jul 2017 Ofir Lindenbaum, Moshe Salhov, Arie Yeredor, Amir Averbuch

We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning.

Dimensionality Reduction General Classification

Incomplete Pivoted QR-based Dimensionality Reduction

no code implementations12 Jul 2016 Amit Bermanis, Aviv Rotbart, Moshe Salhov, Amir Averbuch

The dictionary enables to have a natural extension of the low-dimensional embedding to out-of-sample data points, which gives rise to a distortion-based criterion for anomaly detection.

Anomaly Detection Dimensionality Reduction +1

Multi-View Kernel Consensus For Data Analysis

no code implementations28 Jun 2016 Moshe Salhov, Ofir Lindenbaum, Yariv Aizenbud, Avi Silberschatz, Yoel Shkolnisky, Amir Averbuch

Data analysis methods aim to uncover the underlying low dimensional structure imposed by the low dimensional hidden parameters by utilizing distance metrics that consider the set of attributes as a single monolithic set.

Gaussian Process Regression for Out-of-Sample Extension

no code implementations7 Mar 2016 Oren Barkan, Jonathan Weill, Amir Averbuch

Many of the existing methods produce a low dimensional representation that attempts to describe the intrinsic geometric structure of the original data.

Diffusion Representations

no code implementations19 Nov 2015 Moshe Salhov, Amit Bermanis, Guy Wolf, Amir Averbuch

In this paper, we present a representation framework for data analysis of datasets that is based on a closed-form decomposition of the measure-based kernel.

MultiView Diffusion Maps

no code implementations23 Aug 2015 Ofir Lindenbaum, Arie Yeredor, Moshe Salhov, Amir Averbuch

The multi-view dimensionality reduction is achieved by defining a cross-view model in which an implied random walk process is restrained to hop between objects in the different views.

Anomaly Detection Dimensionality Reduction

Randomized LU decomposition: An Algorithm for Dictionaries Construction

no code implementations17 Feb 2015 Aviv Rotbart, Gil Shabat, Yaniv Shmueli, Amir Averbuch

Such approach is harder to deceive and we show that only a few file fragments from a whole file are needed for a successful classification.

General Classification

Adaptive Compressed Tomography Sensing

no code implementations CVPR 2013 Oren Barkan, Jonathan Weill, Amir Averbuch, Shai Dekel

One of the main challenges in Computed Tomography (CT) is how to balance between the amount of radiation the patient is exposed to during scan time and the quality of the CT image.

Computed Tomography (CT)

Video Segmentation via Diffusion Bases

no code implementations1 May 2013 Dina Dushnik, Alon Schclar, Amir Averbuch

A common approach performs background subtraction, which identifies moving objects as the portion of a video frame that differs significantly from a background model.

Video Segmentation Video Semantic Segmentation

Missing Entries Matrix Approximation and Completion

no code implementations27 Feb 2013 Gil Shabat, Yaniv Shmueli, Amir Averbuch

The approximation constraint can be any whose approximated solution is known for the full matrix.

Matrix Completion

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