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Showing 1 to 12 of 12 for “"tensor factorization"”.
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Tensor factorization in civil infrastructure systems
… multiway data analysis, also known as tensor factorization are utilized to address the multiway nature of data from the National Bridge Inventory (NBI). The NBI is the largest bridge database with detailed information on the condition and general attributes of bridges nationwide. …
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Constrained Matrix and Tensor Factorization: Theory, Algorithms, and Applications
This dissertation studies constrained matrix and tensor factorization problems which appear in the context of estimating factor analysis models used in signal processing and machine learning. Factor analysis dates back to the celebrated principal component analysis (PCA), which provides the optimal …
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Scalable Bayesian Matrix and Tensor Factorization for Discrete Data
<p>Matrix and tensor factorization methods decompose the observed matrix and tensor data into a set of factor matrices. They provide a useful way to extract latent factors or features from complex data, and also to predict missing data. Matrix and tensor factorization has drawn significant …
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Phase difference and tensor factorization models for audio source separation
Made available in DSpace on 2017-03-01T15:46:01Z (GMT). No. of bitstreams: 2 TRAA-DISSERTATION-2016.pdf: 12461339 bytes, checksum: aeab068ca641be012f3b6c8b1f19d354 (MD5) LICENSE.txt: 4210 bytes, checksum: 63ac2ac06dfd739a5099ea28ac00e136 (MD5) Previous issue date: 2016-10-10
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Uma investigação sobre métodos de separação cega de fontes sonoras envolvendo representações não-negativas e diversidade espacial
… source separation using non-negative matrix factorization and direction-of-arrival-based spatial covariance model), whose model represents the state of the art, taking in consideration not only the characteristics of the sources but also the enviroment into which they were captured on; and …
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Blind regression : understanding collaborative filtering from matrix completion to tensor completion
… easily extends to the setting of higher-order tensors and we present our algorithm for tensor completion. The result from real-world application of image inpainting demonstrates that our method is competitive with the state-of-the-art tensor factorization approaches in terms of predictive …
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Computational visual reality
… and capture frameworks that use non-negative tensor factorization and dictionary-based sparse reconstruction, respectively, in conjunction with the co-design of algorithms, optics, and electronics to allow compressive, simultaneous, light field display and capture.
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Challenges in recommender systems : scalability, privacy, and structured recommendations
… we extend the method to non-negative matrix factorization (NMF) and dictionary learning for sparse coding. Privacy is another important issue in RS. Indeed, there is an inherent trade-off between accuracy of recommendations and the extent to which users are willing to release information …
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Exploiting spatial and spectral information for audio source separation and speaker diarization
… spectral modeling based on Nonnegative Matrix Factorization is adopted to represent the source signals. The parameters of Gaussian model-based source separation are estimated in sense of Maximum-Likelihood using a Generalized Expectation-Maximization algorithm by applying supervised Nonnegative …
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Breaking the curse of dimensionality in electronic structure methods: towards optimal utilization of the canonical polyadic decomposition
Despite the fact that higher-order tensors (HOTs) plague electronic structure methods and severely limits the modeling of interesting chemistry problems, introduction and application of higher-order tensor (HOT) decompositions, specifically the canonical polyadic (CP) decomposition, is fairly …
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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 …