Proceedings Article10.1109/ICIP.2010.5654055
Tensor error correction for corrupted values in visual data
Yin Li,Yue Zhou,Junchi Yan,Jie Yang,Xiangjian He +4 more
- 03 Dec 2010
- pp 2321-2324
TL;DR: This work considers the problem of recovering a tensor L of visual data from its corrupted observations X = L + S, where the corrupted entries S are unknown and unbounded, but are assumed to be sparse.
read more
Abstract: The multi-channel image or the video clip has the natural form of tensor. The values of the tensor can be corrupted due to noise in the acquisition process. We consider the problem of recovering a tensor L of visual data from its corrupted observations X = L + S, where the corrupted entries S are unknown and unbounded, but are assumed to be sparse. Our work is built on the recent studies about the recovery of corrupted low-rank matrix via trace norm minimization. We extend the matrix case to the tensor case by the definition of tensor trace norm in [6]. Furthermore, the problem of tensor is formulated as a convex optimization, which is much harder than its matrix form. Thus, we develop a high quality algorithm to efficiently solve the problem. Our experiments show potential applications of our method and indicate a robust and reliable solution.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Tensor completion for estimating missing values in visual data
Ji Liu,Przemyslaw Musialski,Peter Wonka,Jieping Ye +3 more
- 01 Sep 2009
TL;DR: The contribution of this paper is to extend the matrix case to the tensor case by proposing the first definition of the trace norm for tensors and building a working algorithm to estimate missing values in tensors of visual data.
Smooth PARAFAC Decomposition for Tensor Completion
TL;DR: This paper considers “smoothness” constraints as well as low-rank approximations and proposes an efficient algorithm for performing tensor completion that is particularly powerful regarding visual data.
309
On Estimating Air Pollution from Photos Using Convolutional Neural Network
Chao Zhang,Junchi Yan,Changsheng Li,Xiaoguang Rui,Liang Liu,Rongfang Bie +5 more
- 01 Oct 2016
TL;DR: An effective convolutional neural network is devised to estimate air's quality based on photos to alleviate the vanishing gradient issue effectively and a modified activation function is developed for photo based air pollution estimation.
119
Sales pipeline win propensity prediction: A regression approach
Junchi Yan,Min Gong,Changhua Sun,Jin Huang,Stephen M. Chu +4 more
- 11 May 2015
TL;DR: In this article, a machine learning-based unified framework for sales opportunity win propensity prediction is proposed, aimed at addressing the challenge of the relatively small number of B2B transactions, noisy data, and the fast-changing market environment pose challenges to effective predictive modeling.
27
•Posted Content
Sales pipeline win propensity prediction: a regression approach
TL;DR: A machine learning-based unified framework for sales opportunity win propensity prediction, aimed at addressing challenges to effective predictive modeling, is proposed and demonstrated the efficacy of the proposed system using data from a top-500 enterprize in the business-to-business market.
References
Robust Face Recognition via Sparse Representation
TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
Robust principal component analysis
TL;DR: In this paper, the authors prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the e1 norm.
Exact Matrix Completion via Convex Optimization
TL;DR: It is proved that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries, and that objects other than signals and images can be perfectly reconstructed from very limited information.
A Singular Value Thresholding Algorithm for Matrix Completion
TL;DR: This paper develops a simple first-order and easy-to-implement algorithm that is extremely efficient at addressing problems in which the optimal solution has low rank, and develops a framework in which one can understand these algorithms in terms of well-known Lagrange multiplier algorithms.
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Michael Elad,Michal Aharon +1 more
TL;DR: This work addresses the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image, and uses the K-SVD algorithm to obtain a dictionary that describes the image content effectively.
6.2K