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Estimating a Common Period for a Set of Irregularly Sampled Functions with Applications to Periodic Variable Star Data
James P. Long, Eric C. Chi, Richard G. Baraniuk
We consider the estimation of a common period for a set of functions sampled at irregular intervals. The problem arises in astronomy, where the functions represent a star's brightn…
Quantized Matrix Completion for Personalized Learning
Andrew S. Lan, Christoph Studer, Richard G. Baraniuk
The recently proposed SPARse Factor Analysis (SPARFA) framework for personalized learning performs factor analysis on ordinal or binary-valued (e.g., correct/incorrect) graded lear…
Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics
Andrew S. Lan, Christoph Studer, Andrew E. Waters +1
Machine learning offers novel ways and means to design personalized learning systems wherein each student's educational experience is customized in real time depending on their bac…
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit
Ali Ayremlou, Thomas Goldstein, Ashok Veeraraghavan +1
Sparse approximations using highly over-complete dictionaries is a state-of-the-art tool for many imaging applications including denoising, super-resolution, compressive sensing, l…
-POD: A Method for -Means Clustering of Missing Data
Jocelyn T. Chi, Eric C. Chi, Richard G. Baraniuk
The -means algorithm is often used in clustering applications but its usage requires a complete data matrix. Missing data, however, is common in many applications. Mainstream ap…
A Field Guide to Forward-Backward Splitting with a FASTA Implementation
Tom Goldstein, Christoph Studer, Richard Baraniuk
Non-differentiable and constrained optimization play a key role in machine learning, signal and image processing, communications, and beyond. For high-dimensional minimization prob…