Noisy Tensor Completion for Tensors With a Sparse Canonical Polyadic Factor



Noisy Tensor Completion for Tensors With a Sparse Canonical Polyadic Factor

Noisy Tensor Completion for Tensors With a Sparse Canonical Polyadic Factor
Research Paper / IEEE International Symposium on Information Theory (ISIT) / Feb 2017 / Image Processing, Machine/Deep Learning/AI

“To be considered for the 2017 IEEE Jack Keil Wolf ISIT Student Paper Award.” In this paper we study the problem of noisy tensor completion for tensors that admit a canonical polyadic or CANDE-COMP/PARAFAC (CP) decomposition with one of the factors being sparse. We present general theoretical error bounds for an estimate obtained by using a complexity-regularized maximum likelihood principle and then instantiate these bounds for the case of additive white Gaussian noise. We also provide an ADMM-type algorithm for solving the complexity-regularized maximum likelihood problem and validate the theoretical finding via experiments on synthetic data set.

Download Now


Related Content

Metagrating solutions for full color single-plate waveguide combiner
RESEARCH PAPER / Feb 2022 / Immersive/AR/VR/MR, Image Processing
In this work we propose several full-color metagrating solutions for single waveguide-based Augmented and Virtual Reality near-eye display systems. The presented solutions are based on a combination of reflective and/or transmissive diffraction gratings inside or outside a waveguide. The proposed designs have high intensity across a wide angular range. Applying...
An Annotated Video Dataset for Computing Video Memorability
RESEARCH PAPER / Dec 2021 / Image processing
Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1,275 users manually annotated each video multiple times to indicate both longterm and short-term memorability of the videos. The annotations were gathered as part of an online memory game and...
Compact and Adaptive Multiplane Images for View Synthesis
RESEARCH PAPER / Sep 2021 / Video coding, Machine learning/ Deep learning /Artificial Intelligence, Image processing, Computer Graphics
Recently, learning methods have been designed to create Multiplane Images (MPIs) for view synthesis. While MPIs are extremely powerful and facilitate high quality renderings, a great amount of memory is required, making them impractical for many applications. In this paper, we propose a learning method that optimizes the available memory...
Enabling the Technology of Tomorrow
VIDEO / Mar 2023
Unpacking the Layers of AI Impact in 5G
WEBINAR / Feb 2023
Getting tough on patent holdout – Time for solutions
WEBINAR / Dec 2022
InterDigital’s Energy Aware Media Team Awarded Best...
BLOG / May 2023 / Posted By: InterDigital Comms
InterDigital’s Chathura Sarathchandra argues the imp...
BLOG / May 2023 / Posted By: InterDigital Comms
World IP Day: Putting InterDigital’s Female Engineer...
BLOG / Apr 2023 / Posted By: InterDigital Comms

ready to
learn more?

CONTACT INVESTORS
InterDigital logo in white

   © COPYRIGHT 2023 INTERDIGITAL, INC. ALL RIGHTS RESERVED.