5 citations · 11 across the 8 of their papers we have counts for
8 papers
Learning the Geometry of Data: A Mathematical Review of Shape Space Analysis
Gary P. T. Choi, Khanh Dao Duc, Shira Faigenbaum-Golovin +6
A central objective of machine learning is to identify structure and patterns in data. Advances in data acquisition have increasingly produced datasets whose observations possess r…
Studying Morphological Variation: Exploring the Shape Space in Evolutionary Anthropology
Shira Faigenbaum-Golovin, Ingrid Daubechies
We present results of a long-term team collaboration of mathematicians and biologists. We focus on building a mathematical framework for the shape space constituted by a collection…
Critical biblical studies via word frequency analysis: unveiling text authorship
Shira Faigenbaum-Golovin, Alon Kipnis, Axel Bühler +3
The Bible, a product of an extensive and intricate process of oral-written transmission spanning centuries, obscures the contours of its earlier recensions. Debate rages over deter…
Image Quality Assessment: Learning to Rank Image Distortion Level
Shira Faigenbaum-Golovin, Or Shimshi
Over the years, various algorithms were developed, attempting to imitate the Human Visual System (HVS), and evaluate the perceptual image quality. However, for certain image distor…
Neural Network Approximation of Refinable Functions
Ingrid Daubechies, Ronald DeVore, Nadav Dym +6
In the desire to quantify the success of neural networks in deep learning and other applications, there is a great interest in understanding which functions are efficiently approxi…
Manifold Repairing, Reconstruction and Denoising from Scattered Data in High-Dimension
Shira Faigenbaum-Golovin, David Levin
We consider a problem of great practical interest: the repairing and recovery of a low-dimensional manifold embedded in high-dimensional space from noisy scattered data. Suppose th…
Approximation of Functions on Manifolds in High Dimension from Noisy Scattered Data
Shira Faigenbaum-Golovin, David Levin
In this paper, we consider the fundamental problem of approximation of functions on a low-dimensional manifold embedded in a high-dimensional space, with noise affecting both in th…
Manifold Reconstruction and Denoising from Scattered Data in High Dimension via a Generalization of -Median
Shira Faigenbaum-Golovin, David Levin
In this paper, we present a method for denoising and reconstruction of low-dimensional manifold in high-dimensional space. We suggest a multidimensional extension of the Locally Op…