5 papers · 1 filter
A Latent-Variable Formulation of the Poisson Canonical Polyadic Tensor Model: Maximum Likelihood Estimation and Fisher Information
Carlos Llosa-Vite, Daniel M. Dunlavy, Richard B. Lehoucq +2
We establish parameter inference for the Poisson canonical polyadic (PCP) model of tensor count data through a latent-variable formulation. Our approach exploits the property that…
Near-Efficient and Non-Asymptotic Multiway Inference
Oscar López, Arvind Prasadan, Carlos Llosa-Vite +2
We establish non-asymptotic efficiency guarantees for tensor decomposition-based inference in count data models. Under a Poisson framework, we consider two related goals: (i) param…
The Poisson tensor completion parametric estimator
Daniel M. Dunlavy, Richard B. Lehoucq, Carolyn D. Mayer +1
We introduce the Poisson tensor completion (PTC) estimator that exploits inter-sample relationships to compute a low-rank Poisson tensor decomposition of the frequency histogram fo…
Sparse Equisigned PCA: Algorithms and Performance Bounds in the Noisy Rank-1 Setting
Arvind Prasadan, Raj Rao Nadakuditi, Debashis Paul
Singular value decomposition (SVD) based principal component analysis (PCA) breaks down in the high-dimensional and limited sample size regime below a certain critical eigen-SNR th…
Time Series Source Separation using Dynamic Mode Decomposition
Arvind Prasadan, Raj Rao Nadakuditi
The Dynamic Mode Decomposition (DMD) extracted dynamic modes are the non-orthogonal eigenvectors of the matrix that best approximates the one-step temporal evolution of the multiva…