Search Results for author:
Found 16 papers, 6 papers with code
G-invariant diffusion maps
The diffusion maps embedding of data lying on a manifold have shown success in tasks ranging from dimensionality reduction and clustering, to data visualization.
The G-invariant graph Laplacian
We introduce the G-invariant graph Laplacian that generalizes the graph Laplacian by accounting for the action of the group on the data set.
Signal enhancement for two-dimensional cryo-EM data processing
Different tasks in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM) require enhancing the quality of the highly noisy raw images.
Three-Dimensional Alignment of Density Maps in Cryo-Electron Microscopy
A common task in cryo-electron microscopy (cryo-EM) data processing is to compare three-dimensional density maps of macromolecules.
ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM
In this paper, we present ASOCEM (Automatic Segmentation Of Contaminations in cryo-EM), an automatic method to detect and segment contaminations, which requires as an input only the approximated particle size.
A Perturbation-Based Kernel Approximation Framework
Various kernel approximation methods were proposed to overcome this issue, with the most prominent method being the Nystr{\"o}m method.
KLT Picker: Particle Picking Using Data-Driven Optimal Templates
We present the KLT (Karhunen Loeve Transform) picker, which is fully automatic and requires as an input only the approximated particle size.
ROIPCA: An online memory-restricted PCA algorithm based on rank-one updates
Principal components analysis (PCA) is a fundamental algorithm in data analysis.
Multi-reference factor analysis: low-rank covariance estimation under unknown translations
Solving this problem allows to discover low-rank structures masked by the existence of translations (which act as nuisance parameters), with direct application to Principal Components Analysis (PCA).
Statistics Theory Data Structures and Algorithms Information Theory Information Theory Statistics Theory
The steerable graph Laplacian and its application to filtering image data-sets
Essentially, the steerable GL extends the standard GL by accounting for all (infinitely-many) planar rotations of all images.
A max-cut approach to heterogeneity in cryo-electron microscopy
In this paper, we attempt to make the first steps towards rigorous mathematical analysis of the heterogeneity problem in cryo-electron microscopy.
Steerable Principal Components for Space-Frequency Localized Images
This paper describes a fast and accurate method for obtaining steerable principal components from a large dataset of images, assuming the images are well localized in space and frequency.
Multi-View Kernel Consensus For Data Analysis
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.
Machine olfaction using time scattering of sensor multiresolution graphs
In this paper we construct a learning architecture for high dimensional time series sampled by sensor arrangements.
Fast Steerable Principal Component Analysis
Cryo-electron microscopy nowadays often requires the analysis of hundreds of thousands of 2D images as large as a few hundred pixels in each direction.
An algorithm for improving Non-Local Means operators via low-rank approximation
We present a method for improving a Non Local Means operator by computing its low-rank approximation.
