13 citations · 54 across the 7 of their papers we have counts for
7 papers
Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning
Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
We consider the problem of factorizing a structured 3-way tensor into its constituent Canonical Polyadic (CP) factors. This decomposition, which can be viewed as a generalization o…
Target-based Hyperspectral Demixing via Generalized Robust PCA
Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
Localizing targets of interest in a given hyperspectral (HS) image has applications ranging from remote sensing to surveillance. This task of target detection leverages the fact th…
TensorMap: Lidar-Based Topological Mapping and Localization via Tensor Decompositions
Sirisha Rambhatla, Nikos D. Sidiropoulos, Jarvis Haupt
We propose a technique to develop (and localize in) topological maps from light detection and ranging (Lidar) data. Localizing an autonomous vehicle with respect to a reference map…
A Dictionary Based Generalization of Robust PCA
Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
We analyze the decomposition of a data matrix, assumed to be a superposition of a low-rank component and a component which is sparse in a known dictionary, using a convex demixing…
NOODL: Provable Online Dictionary Learning and Sparse Coding
Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
We consider the dictionary learning problem, where the aim is to model the given data as a linear combination of a few columns of a matrix known as a dictionary, where the sparse w…
A Dictionary-Based Generalization of Robust PCA with Applications to Target Localization in Hyperspectral Imaging
Sirisha Rambhatla, Xingguo Li, Jineng Ren +1
We consider the decomposition of a data matrix assumed to be a superposition of a low-rank matrix and a component which is sparse in a known dictionary, using a convex demixing met…
Semi-blind Source Separation via Sparse Representations and Online Dictionary Learning
Sirisha Rambhatla, Jarvis D. Haupt
This work examines a semi-blind single-channel source separation problem. Our specific aim is to separate one source whose local structure is approximately known, from another a pr…