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102,494
Memoryefficient A*search using sparse embeddings
 IN: PROC. ACM 17TH INTERNATIONAL WORKSHOP ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM GIS
, 2010
"... When searching for optimal paths in a network, algorithms like A*search need an approximation of the minimal costs between the current node and a target node. A reference node embedding is a universal method for making such an approximation working for any type of positive edge weights. A drawback ..."
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Cited by 2 (2 self)
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When searching for optimal paths in a network, algorithms like A*search need an approximation of the minimal costs between the current node and a target node. A reference node embedding is a universal method for making such an approximation working for any type of positive edge weights. A drawback
Robust Sparse Embedding and Reconstruction via Dictionary Learning
"... Abstract—A novel approach is developed for nonlinear compression and reconstruction of high or even infinitedimensional signals living on a smooth but otherwise unknown manifold. Compression is effected through affine embeddings to lowerdimensional spaces. These embeddings are obtained via linea ..."
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Abstract—A novel approach is developed for nonlinear compression and reconstruction of high or even infinitedimensional signals living on a smooth but otherwise unknown manifold. Compression is effected through affine embeddings to lowerdimensional spaces. These embeddings are obtained via
1 A Sparse Embedding and Least Variance Encoding Approach to Hashing
"... Abstract—Hashing is becoming increasingly important in largescale image retrieval for fast approximate similarity search and efficient data storage. Many popular hashing methods aim to preserve the kNN graph of high dimensional data points in the low dimensional manifold space, which is however dif ..."
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Cited by 1 (1 self)
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difficult to achieve when the number of samples is big. In this paper, we propose an effective and efficient hashing approach by sparsely embedding a sample in the training sample space and encoding the sparse embedding vector over a learned dictionary. To this end, we partition the sample space
Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems
 IEEE Journal of Selected Topics in Signal Processing
, 2007
"... Abstract—Many problems in signal processing and statistical inference involve finding sparse solutions to underdetermined, or illconditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined wi ..."
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Cited by 524 (15 self)
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Abstract—Many problems in signal processing and statistical inference involve finding sparse solutions to underdetermined, or illconditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
 Advances in Neural Information Processing Systems 14
, 2001
"... Drawing on the correspondence between the graph Laplacian, the LaplaceBeltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in a higher ..."
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Cited by 664 (8 self)
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Drawing on the correspondence between the graph Laplacian, the LaplaceBeltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in a
Sparse Embedding: A Framework For Sparsity Promoting Dimensionality Reduction
"... Abstract. We introduce a novel framework, called sparse embedding (SE), for simultaneous dimensionality reduction and dictionary learning. We formulate an optimization problem for learning a transformation from the original signal domain to a lowerdimensional one in a way that preserves the sparse ..."
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Cited by 6 (4 self)
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Abstract. We introduce a novel framework, called sparse embedding (SE), for simultaneous dimensionality reduction and dictionary learning. We formulate an optimization problem for learning a transformation from the original signal domain to a lowerdimensional one in a way that preserves the sparse
Data Streams: Algorithms and Applications
, 2005
"... In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerg ..."
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Cited by 538 (22 self)
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emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudorandom computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic
Prediction of problematic complexes from PPI networks: sparse, embedded, and small complexes
, 2015
"... ..."
For Most Large Underdetermined Systems of Linear Equations the Minimal ℓ1norm Solution is also the Sparsest Solution
 Comm. Pure Appl. Math
, 2004
"... We consider linear equations y = Φα where y is a given vector in R n, Φ is a given n by m matrix with n < m ≤ An, and we wish to solve for α ∈ R m. We suppose that the columns of Φ are normalized to unit ℓ 2 norm 1 and we place uniform measure on such Φ. We prove the existence of ρ = ρ(A) so that ..."
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Cited by 560 (10 self)
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. In contrast, heuristic attempts to sparsely solve such systems – greedy algorithms and thresholding – perform poorly in this challenging setting. The techniques include the use of random proportional embeddings and almostspherical sections in Banach space theory, and deviation bounds for the eigenvalues
Image denoising by sparse 3D transformdomain collaborative filtering
 IEEE TRANS. IMAGE PROCESS
, 2007
"... We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call “groups.” Collaborative filtering is a special procedure d ..."
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Cited by 422 (32 self)
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We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call “groups.” Collaborative filtering is a special procedure
Results 1  10
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102,494