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Found 9 papers, 6 papers with code
Manifold learning with arbitrary norms
Manifold learning methods play a prominent role in nonlinear dimensionality reduction and other tasks involving high-dimensional data sets with low intrinsic dimensionality.
Wasserstein K-Means for Clustering Tomographic Projections
Motivated by the 2D class averaging problem in single-particle cryo-electron microscopy (cryo-EM), we present a k-means algorithm based on a rotationally-invariant Wasserstein metric for images.
Product Manifold Learning
Mathematically, if the parameter space of each continuous independent motion is a manifold, then their combination is known as a product manifold.
Unsupervised particle sorting for high-resolution single-particle cryo-EM
Single-particle cryo-Electron Microscopy (EM) has become a popular technique for determining the structure of challenging biomolecules that are inaccessible to other technologies.
Earthmover-based manifold learning for analyzing molecular conformation spaces
In this paper, we propose a novel approach for manifold learning that combines the Earthmover's distance (EMD) with the diffusion maps method for dimensionality reduction.
Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes
An important challenge in cryo-EM is the reconstruction of non-rigid molecules with parts that move and deform.
On the cross-validation bias due to unsupervised pre-processing
Cross-validation is the de facto standard for predictive model evaluation and selection.
Beyond Trees: Classification with Sparse Pairwise Dependencies
The resulting classifier is linear in the log-transformed univariate and bivariate densities that correspond to the tree edges.
Minimax-optimal semi-supervised regression on unknown manifolds
We consider semi-supervised regression when the predictor variables are drawn from an unknown manifold.
