Low Rank
Rank Determination for Low-Rank Data Completion
... practical low-rank data completion problems, the rank may not be known a ...the rank based on the given entries, where it is assumed that the original data is generically chosen from the ...
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Learning Robust and Discriminative Subspace With Low-Rank Constraints
... in low-rank modeling provide effective solutions for removing noise or outliers contained in sample sets, which motivates us to take advantage of low-rank constraints in order to exploit ...
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Bootstrap-Based Regularization for Low-Rank Matrix Estimation
... These two examples already reveal a variety of different phenomena. On one hand, sta- ble autoencoding with the Gaussian noise model (7) reduces to a singular-value shrinkage estimator (Section 2), and thus leads us back ...
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Greedy low-rank algorithm for spatial connectome regression
... nonnegative low-rank factors or enforcing that the low-rank approximation UV ≈ W is nonnegative—a nonlinear constraint—is a much harder goal to ...nonnegative low-rank ...
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Low-Rank Sparse Learning for Robust Visual Tracking
... Inspired by the above work, we propose a computationally efficient tracking method that capitalizes on sparse and low-rank representation in a particle filter framework. This method is denoted as the ...
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Reconstruction of a high-dimensional low-rank matrix
... r is unknown. We assume r (< min{d, n}) is fixed. For high-dimensional data, the estimation of the low-rank matrix is quite important in many fields such as genomics, image denoising, recommendation ...
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EFFECTIVE DIFFUSIVITY OF MOISTURE IN LOW RANK COAL
... 30%. Low rank coal share will increase in the ...Poland low rank coal’s share in energy production is currently ...from low rank coal is 20-30% lower than that of black coal, ...
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Low Rank Tensors for Scoring Dependency Structures
... our low-rank model are consistent across lan- guages: results for the first order parser are better on 11 out of 14 ...without low-rank) with our full model (with low-rank), we ...
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Low Rank Positive Approximants of Symmetric Matrices
... Once Theorem 22 is proved, it is possible to use this result to derive Theorems 15-18. Yet the direct proofs that we give clearly illustrate why these theorems work. In fact, the proof of Theorem 15 paves the way for the ...
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Obfuscating Low-Rank Matrix Branching Programs
... If we are to obtain a method for obfuscation that works with low-rank matrices, then we would need to avoid Kilian’s theorem entirely, deviating from all previous analyses of obfuscation. Our Techniques. ...
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Spectral thresholding quantum tomography for low rank states
... ‘effectively’ low rank, ...the rank-penalized estimator addressing this problem, and show that its theoretical and practical performance is superior to the LSE, and is close to what one would expect ...
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Lower bounds for the low rank matrix approximation
... the low-rank component and E ∈ R m×n is the perturbation compo- nent of ...best rank-r estimation of A by solving the following constrained optimization via SVD of D and then projecting the columns ...
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Low Rank Tensors for Verbs in Compositional Distributional Semantics
... While in our experiments the prediction-based vectors outperform the count-based vectors on both tasks for most models, Levy et al. (2015) in- dicate that tuning hyperparameters of the count- based vectors may be able to ...
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Simulation of quantum circuits by low-rank stabilizer decompositions
... Such low-rank decompositions may lead to more efficient simulation algorithms for Clifford+CCZ ...stabilizer rank of n-qubit magic states scaling exponentially with ...
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Embedding Lexical Features via Low Rank Tensors
... eters by approximating the parameter tensor with a low-rank tensor: the Tucker approximation of Yu et al. (2015) but applied to each embedding type (view), or the Canonical/Parallel-Factors Decompo- sition ...
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Hierarchical Low Rank Tensors for Multilingual Transfer Parsing
... al., 2013; Yu et al., 2013a). While these methods can automatically combine atomic features into a compact composite representation, they cannot take into account constraints on feature combina- tion. In contrast, our ...
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Low Rank Approximations of Second Order Document Representations
... Our core contributions are in the use of second-order information—covariance of the word vectors—for improving the representations. To our best knowledge, there is quite limited previous work in this direction. Torki ...
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Low-Rank Kernel Learning with Bregman Matrix Divergences
... study low-rank matrix nearness problems, with a focus on learning low-rank positive semidefinite (kernel) matrices for machine learning ...the rank of the input ...learning ...
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Online Learning in the Embedded Manifold of Low-rank Matrices
... a low-rank model of rank 30, using the best 16660 features, was significantly more precise than learning a much fuller model of rank 100 and 5000 features, or a model using the full 50000 word ...
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The Algebraic Combinatorial Approach for Low-Rank Matrix Completion
... common low-rank as- sumption - that is, under the model assumption (or approximation) that there is an under- lying complete matrix of some low rank r from which the partial observations ...
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