4 papers
Group Invariant Spectral Embedding
Yeari Vigder, Paulina Hoyos, David Thong +3
Spectral embedding methods are widely used for dimensionality reduction and clustering of high-dimensional datasets with intrinsic low-dimensional structures. Although many dataset…
Optimized Sequential Testing for Binary Ensemble Classifiers
Joseph Kalman, Amit Moscovich
Ensemble classifiers are predictive models that combine the results of simpler base models, often by majority vote. A classic example is random forests, which combine the predictio…
Wasserstein K-Means for Clustering Tomographic Projections
Rohan Rao, Amit Moscovich, Amit Singer
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 metr…
Product Manifold Learning
Sharon Zhang, Amit Moscovich, Amit Singer
We consider problems of dimensionality reduction and learning data representations for continuous spaces with two or more independent degrees of freedom. Such problems occur, for e…