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Mathematics
Eigenvector
39%
Manifold
36%
Laplace Operator
32%
Matrix (Mathematics)
31%
Principal Component Analysis
29%
Convex
27%
Dimensional Structure
25%
Edge
24%
Noise Ratio
22%
Time Mean
20%
Reflecting Boundary
20%
Covariance
20%
Numerical Experiment
19%
Maximum Likelihood Estimator
18%
Covariance Matrix
18%
Probability
18%
Variance
16%
Intrinsic Property
15%
Graph Laplacian
15%
Autocorrelation
15%
Experimental Data
14%
Boundary Layer
13%
Stochastics
13%
Langevin Equation
13%
Signal Processing
13%
Dimensionality Reduction
13%
Brownian Particle
13%
Least Squares Method
12%
Brownian Motion
12%
Median
11%
Maximum Likelihood
11%
Asymptotic Expansion
11%
Uniform Distribution
11%
Orthogonal Matrix
10%
Nonuniform
10%
Residuals
9%
Alternating Direction Method of Multipliers
9%
Power Spectra
9%
Independent Component
9%
Bounded Domain
9%
Complete Matrix
8%
Mean Square Error
8%
Dimensional Riemannian Manifold
8%
Algebraic Structure
8%
Approximates
8%
Intrinsic Geometry
7%
Wide Range
7%
Compact Group
7%
Rare Event
7%
Walsh Transform
7%
Exponential Family
7%
Synthetic Data
7%
Real Life
7%
Partial Differential Equation
7%
Wavelet
7%
Singular Perturbations
7%
Unit Time
7%
Diffusion Approximation
7%
Diffusion
7%
relaxation time τ
7%
Euclidean Distance
7%
Time Evolution
7%
Dimensional Model
7%
Boundary Condition
7%
Simulated Data
7%
Linear System
6%
Linear Inverse Problems
6%
Regularization
6%
Random Noise
6%
Running Time
6%
3D-Reconstruction
6%
Grothendieck
6%
Planck Equation
6%
Gaussian Distribution
6%
Principal Components
5%
Minimizes
5%
Sparse Graphs
5%
Orthogonal Group
5%
Eigenvalue
5%
Convolution
5%
Two Dimensions
5%
Conditionals
5%
Numerical Analysis
5%
Friedrich Wilhelm Bessel
5%
Singular Perturbation Problem
5%
Riemannian Manifolds
5%
Dimensional Case
5%
Representation Theory
5%
Covariance Matrix Estimation
5%
Sparse Matrix
5%
Transfer Function
5%
Bayesian
5%
Pairwise Distance
5%
Spatial Distribution
5%
Iteratively reweighted least square
5%
Cyclic Shift
5%
Rigidity
5%
Mathematical Analysis
5%
Illustrative Example
5%
Three-Dimension
5%
Keyphrases
Cryogenic Electron Microscopy
100%
Multi-reference Alignment
26%
Noise Level
20%
Single-particle Reconstruction
19%
Single-particle Cryo-electron Microscopy
19%
Manifold Learning
18%
Viewing Angle
17%
Principal Coordinate Analysis (PCoA)
16%
Semidefinite Programming
16%
Mean Escape Time
15%
Exact Recovery
14%
Graph Laplacian
13%
Three-dimensional (3D)
13%
Macromolecules
13%
Covariance Estimation
12%
Diffusion Maps
12%
Noisy Measurements
12%
Particle picking
11%
Random Projection
11%
Sample Complexity
11%
3D Structure
11%
Autocorrelation Analysis
10%
Covariance Matrix
9%
Numerical Experiments
9%
Multi-target Detection
9%
Experimental Dataset
9%
Single-particle cryo-EM
9%
Structural Variability
9%
Semidefinite Relaxation
9%
Electron Microscopy Images
9%
Projection Image
8%
Structure Determination
8%
Laplacian
8%
Non-uniform Distribution
8%
Eigenvector
8%
Denoising
8%
High Noise
8%
Biological Macromolecules
7%
Autocorrelation
7%
Popular
7%
Target Image
7%
NMR Spectroscopy
7%
Connection Laplacian
7%
Rotation Invariance
7%
Narrow Escape
7%
Walsh Transform
7%
Efficient Recovery
7%
Ab Initio Modeling
7%
Synchronization Problem
7%
Camera Location
7%
Cryo-EM Images
7%
Controller
7%
Center of Mass
7%
Steady State
7%
Protein Channels
7%
Noise Regime
7%
Computational Complexity
7%
Dimensionality Reduction
7%
Conformational Space
6%
Reflecting Boundary
6%
Noisy Projections
6%
Inverse Problem
6%
Non-local Means
6%
Vector Diffusion Maps
6%
Computational Challenges
6%
X-ray Crystallography
6%
Covariance
6%
Langevin Equation
6%
Unidirectional Fluxes
6%
Langevin
6%
Linear Inverse Problems
6%
Boundary Layer
6%
Large Noise
6%
Contrast Transfer Function
5%
Intrinsic Geometry
5%
Structural Biology
5%
Convex Relaxation
5%
Patch Space
5%
Image Denoising
5%
Computational Methods
5%
Anisotropic Diffusion
5%
Site Recovery
5%
2-norm
5%
Point Cloud
5%
Synthetic Data
5%
Sparse Matrix
5%
Pattern Formation
5%
Orthogonal Matrix
5%
Non-linear Principal Component Analysis
5%
Learning Techniques
5%
Boolean
5%
Robustness to Noise
5%
Common Lines
5%
Image Dataset
5%
Computer Vision
5%
2D Classification
5%
High-dimensional Data
5%
Fourier Shell Correlation
5%
In(III)
5%
Orthogonal Procrustes Problem
5%
Computer Science
Principal Components
22%
Component Analysis
19%
Semidefinite Programming
18%
Eigenvector
18%
Signal-to-Noise Ratio
15%
Synchronism
15%
de-noising
14%
Reconstruction Problem
13%
Covariance Matrix
13%
Dimensional Structure
13%
Coordinate System
11%
Viewing Direction
11%
Optimization Problem
8%
Reliable Detection
8%
Reconstitution
8%
Convex Optimization
8%
structure-from-motion
8%
Motion Estimation
8%
Location Estimation
8%
Experimental Dataset
7%
Transfer Function
7%
Manifold Learning
7%
Learning Technique
7%
Optical Spectrum
7%
Convex Programming
7%
Synthetic Data
7%
Uniform Distribution
7%
maximum-likelihood
7%
Sensor Network
7%
Convex Relaxation
7%
Approximation (Algorithm)
6%
Vector Diffusion
5%
Least Squares Method
5%
Proof
5%
Synthetic Datasets
5%
Image Restoration
5%
Noise Variance
5%
Theoretical Framework
5%
Estimation Accuracy
5%
3d Reconstruction
5%
Microscopy Data
5%
Detection Rate
5%
