Results 231 to 240 of about 55,177 (267)
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Discrete signal processing on graphs: Graph filters
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013We propose a novel discrete signal processing framework for structured datasets that arise from social, economic, biological, and physical networks. Our framework extends traditional discrete signal processing theory to datasets with complex structure that can be represented by graphs, so that data elements are indexed by graph nodes and relations ...
Aliaksei Sandryhaila, José M. F. Moura
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Discrete uncertainty principles on graphs
2016 50th Asilomar Conference on Signals, Systems and Computers, 2016This paper advances a new way to formulate the uncertainty principle for graphs, by using a non-local measure based on the notion of sparsity. The uncertainty principle is formulated based on the total number of nonzero elements in the signal and its corresponding graph Fourier transform (GFT).
Teke, Oguzhan, Vaidyanathan, P. P.
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Asymmetric Discrete Graph Hashing
Proceedings of the AAAI Conference on Artificial Intelligence, 2017Recently, many graph based hashing methods have been emerged to tackle large-scale problems. However, there exists two major bottlenecks: (1) directly learning discrete hashing codes is an NP-hardoptimization problem; (2) the complexity of both storage and computational time to build a graph with n data points is O(n2). To address these
Xiaoshuang Shi +4 more
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2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
This paper focuses on the problem of hyper-graph matching, by accounting for both unary and higher-order affinity terms. Our method is in line with the linear approximate framework while the problem is iteratively solved in discrete space. It is empirically found more efficient than many extant continuous methods.
Junchi Yan +5 more
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This paper focuses on the problem of hyper-graph matching, by accounting for both unary and higher-order affinity terms. Our method is in line with the linear approximate framework while the problem is iteratively solved in discrete space. It is empirically found more efficient than many extant continuous methods.
Junchi Yan +5 more
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Localization on Discrete Grid Graphs
2011Grid graphs are popular testbeds for planning with incomplete information. In particular, it is studied a fundamental planning problem, localization, to investigate whether gridworlds make good testbeds for planning with incomplete information. It is found empirically that greedy planning methods that interleave planning and plan execution can localize
Gorbenko, Anna +2 more
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AIP Conference Proceedings, 2020
The concept of topology has great significance in the study of spatial objects and their associated problems encountered in Physics. In topology, a discrete space is an example of a topological space in which the points form a discontinuous sequence, meaning they are isolated from each other in a certain sense.
Roswita Amalanathan Maridas +1 more
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The concept of topology has great significance in the study of spatial objects and their associated problems encountered in Physics. In topology, a discrete space is an example of a topological space in which the points form a discontinuous sequence, meaning they are isolated from each other in a certain sense.
Roswita Amalanathan Maridas +1 more
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Locality-constrained discrete graph hashing
Neurocomputing, 2020Abstract Hashing techniques have been widely used for large-scale image retrieval because of its low storage cost and high query speed. Hashing maps similar data onto binary codes with a smaller Hamming distance, which is essentially a discrete optimization problem with constraints.
Wenjie Ying +2 more
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On the Characteristic Graph of a Discrete Symmetric Channel
IEEE Transactions on Information Theory, 2021We present some characterizations of characteristic graphs of row and/or column symmetric channels. We also give a polynomial-time algorithm that decides whether there exists a discrete symmetric channel whose characteristic graph is equal to a given input graph. In addition, we show several applications of our results.
Dariusz Dereniowski, Marcin Jurkiewicz
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Discrete signal processing on graphs: Graph fourier transform
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013We propose a novel discrete signal processing framework for the representation and analysis of datasets with complex structure. Such datasets arise in many social, economic, biological, and physical networks. Our framework extends traditional discrete signal processing theory to structured datasets by viewing them as signals represented by graphs, so ...
Aliaksei Sandryhaila, José M. F. Moura
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Riesz Means on Graphs and Discrete Groups
Potential Analysis, 2011For \(\alpha>0\) and \(R>0\), the Riesz mean of order \(\alpha\) is the operator defined by \[ m_{\alpha,R}(\Delta) = \int_0^2 m_{\alpha,R}(\lambda) \mathrm{d}E_\lambda \] where \(\Delta\) is the discrete Laplacian, \(\mathrm{d}E_\lambda\) is its spectral measure (so that \(\Delta = \int_0^2 \lambda \mathrm{d}E_\lambda\)) and \[ m_{\alpha,R}(\lambda) =
Fotiadis, Anestis +1 more
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