101 citations · 615 across the 44 of their papers we have counts for
6 papers · 1 filter
Streaming Principal Component Analysis From Incomplete Data
Armin Eftekhari, Gregory Ongie, Laura Balzano +1
Linear subspace models are pervasive in computational sciences and particularly used for large datasets which are often incomplete due to privacy issues or sampling constraints. Th…
What to Expect When You Are Expecting on the Grassmannian
Armin Eftekhari, Laura Balzano, Michael B. Wakin
Consider an incoming sequence of vectors, all belonging to an unknown subspace , and each with many missing entries. In order to estimate , it i…
Towards a Theoretical Analysis of PCA for Heteroscedastic Data
David Hong, Laura Balzano, Jeffrey A. Fessler
Principal Component Analysis (PCA) is a method for estimating a subspace given noisy samples. It is useful in a variety of problems ranging from dimensionality reduction to anomaly…
Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation From Undersampled Data
Dejiao Zhang, Laura Balzano
Subspace learning and matrix factorization problems have great many applications in science and engineering, and efficient algorithms are critical as dataset sizes continue to grow…
Leveraging Union of Subspace Structure to Improve Constrained Clustering
John Lipor, Laura Balzano
Many clustering problems in computer vision and other contexts are also classification problems, where each cluster shares a meaningful label. Subspace clustering algorithms in par…
On Learning High Dimensional Structured Single Index Models
Nikhil Rao, Ravi Ganti, Laura Balzano +2
Single Index Models (SIMs) are simple yet flexible semi-parametric models for machine learning, where the response variable is modeled as a monotonic function of a linear combinati…