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Analysis Operator Learning and its Application to Image Reconstruction
TLDR
We present an algorithm for learning an analysis operator from training images based on lp-norm minimization on the set of full rank matrices with normalized columns. Expand
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A Joint Intensity and Depth Co-sparse Analysis Model for Depth Map Super-resolution
TLDR
We introduce a bimodal co-sparse analysis model, which is able to capture the interdependency of registered intensity and depth information. Expand
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Separable Dictionary Learning
TLDR
We present a method for learning dictionaries that are efficiently applicable in image reconstruction tasks. Expand
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Dense disparity maps from sparse disparity measurements
TLDR
We propose a conjugate subgradient method for the arising optimization problem that is applicable to large scale systems and recovers the disparity map efficiently. Expand
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Sample Complexity of Dictionary Learning and Other Matrix Factorizations
TLDR
This paper provides a unified perspective on the sample complexity of several widely used matrix factorization schemes for sparse dictionary learning and signal processing. Expand
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Joint Matrices Decompositions and Blind Source Separation: A survey of methods, identification, and applications
TLDR
Matrix decompositions such as the eigenvalue decomposition (EVD) or the singular value decomposition have a long history in signal processing problems, such as spectral analysis, signal/noise subspace estimation, principal component analysis (PCA), dimensionality reduction, and whitening in independent component analysis. Expand
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Network Volume Anomaly Detection and Identification in Large-Scale Networks Based on Online Time-Structured Traffic Tensor Tracking
TLDR
This paper proposes an online subspace tracking of a Hankelized time-structured traffic tensor for normal flows based on the Candecomp/PARAFAC decomposition exploiting the recursive least squares algorithm. Expand
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A Bimodal Co-sparse Analysis Model for Image Processing
TLDR
In this paper, we propose a bimodal co-sparse analysis model that is able to capture the interdependency of two image modalities. Expand
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Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation
TLDR
We propose a framework to learn convolutional features for texture segmentation when no such training data is available. Expand
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Robust PCA and subspace tracking from incomplete observations using $$\ell _0$$ℓ0-surrogates
TLDR
We present a framework for Robust PCA that is able to recover a low-rank matrix from a data set corrupted by sparse outliers and missing data. Expand
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