Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 13 of 13 for “"tensor completion"”.
-
A multiresolution approach for tensor completion from coarse and partial observations
Existing tensor completion formulation mostly relies on partial observations from a single tensor. However, tensors extracted from real-world data often are more complex due to: (i) Partial observation: Only a small subset of tensor elements are available. (ii) Coarse observation: Some tensor modes …
-
Blind regression : understanding collaborative filtering from matrix completion to tensor completion
… We apply our framework to the problem of matrix completion and give a nonparametric method which, similar to CF, combines the local estimates according to the distance between the neighbors. We use the sample variance of the difference in ratings between neighbors as the proximity of the …
-
Causal Inference for Social and Engineering Systems
… we introduce is connecting causal inference with tensor completion. In particular, we represent the various potential outcomes (i.e., counterfactuals) of interest through an order-3 tensor. The key theoretical results presented are: (i) Formal identification results establishing under what …
-
Blind regression : nonparametric regression for latent variable models via collaborative filtering
… regression, a framework motivated by matrix completion for recommender systems: given m users, n items, and a subset of user-item ratings, the goal is to predict the unobserved ratings given the data, i.e., to complete the partially observed matrix. We posit that user u and movie i have …
-
Algorithms and software for efficient tensor decompositions
Tensors, which generalize vectors and matrices to higher dimensions, provide a powerful framework for representing and analyzing multi-way data arising in science and engineering. Their ability to capture complex, multi-relational structures makes them invaluable in applications ranging from …
-
Challenges in recommender systems : scalability, privacy, and structured recommendations
… a scalable primal dual algorithm for matrix completion based on trace norm regularization. The regularization problem is solved via a constraint generation method that explicitly maintains a sparse dual and the corresponding low rank primal solution. We provide a new dual block coordinate …
-
Themes in numerical tensor calculus
… distinct, but related, aspects of numerical tensor calculus. First, we introduce a simple, black-box compression format for tensors with a multiscale structure. By representing the tensor as a sum of compressed tensors defined on increasingly coarse grids, the format captures low-rank …
-
Simulating Dynamical Systems from Data
… a link between time series analysis and Matrix/Tensor Completion. Second, we develop and analyze an algorithm for change point detection inspired by the factorization structure and based on the cumulative sum (CUSUM) statistic. This work extends the analysis of CUSUM statistics traditionally …
-
Improving Efficiency and Fairness in Machine Learning: a Discrete Optimization Approach
… we present a holistic framework employing tensor completion and robust optimization for prescribing influenza vaccine composition. We also build an optimal classification tree to predict the efficacy of the proposed vaccine in terms of morbidity and mortality rates for different countries. …
-
Modelling Group Recommender Systems
… becomes prohibitively large. We proposed a novel Tensor Completion Cohesion model that can determine the effectiveness of any subgroup formation based on observing only a very limited number of them.
-
Advanced Data Analytics for Quality Assurance of Smart Additive Manufacturing
… image stream data using smooth and sparse tensor completion is proposed and applied to data acquisition of additive manufacturing. The qualified thermal data is able to extract useful information like boundary velocity, thermal gradient, etc. 2. To effectively extract features for high …
-
Optimization Algorithms for Structured Machine Learning and Image Processing Problems
… lasso), unsupervised learning (e.g., robust tensor decompositions), and total-variation image denoising. These algorithms are of wide interest to the optimization, machine learning, and image processing communities. Specifically, (i) we present two algorithms to solve the Group Lasso problem. …
-
High-Dimensional Generative Models for 3D Perception
… a unified framework consisting of a novel tensor data representation, an adaptive feature encoder, and a generative Bayesian network. In the next section, a novel multi-level generative chaotic Recurrent Neural Network (RNN) has been proposed using a sparse tensor structure for image …