5 papers
Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation
Alex Saad-Falcon, Brighton Ancelin, Justin Romberg
Principal component analysis classically requires full -dimensional samples, yet in various applications hardware limits acquisition to a few scalar measurements per sample. We…
Rapid Grassmannian Averaging with Chebyshev Polynomials
Brighton Ancelin, Alex Saad-Falcon, Kason Ancelin +1
We propose new algorithms to efficiently average a collection of points on a Grassmannian manifold in both the centralized and decentralized settings. Grassmannian points are used…
MANGO: Learning Disentangled Image Transformation Manifolds with Grouped Operators
Brighton Ancelin, Yenho Chen, Peimeng Guan +4
Learning semantically meaningful image transformations (i.e. rotation, thickness, blur) directly from examples can be a challenging task. Recently, the Manifold Autoencoder (MAE) p…
Subspace Tracking with Dynamical Models on the Grassmannian
Alex Saad-Falcon, Brighton Ancelin, Justin Romberg
Tracking signals in dynamic environments presents difficulties in both analysis and implementation. In this work, we expand on a class of subspace tracking algorithms which utilize…
Decentralized Feature-Distributed Optimization for Generalized Linear Models
Brighton Ancelin, Sohail Bahmani, Justin Romberg
We consider the "all-for-one" decentralized learning problem for generalized linear models. The features of each sample are partitioned among several collaborating agents in a conn…