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Discrete Optimal Graph Clustering [PDF]
IEEE Transactions on Cybernetics, 2020 Graph based clustering is one of the major clustering methods. Most of it work in three separate steps: similarity graph construction, clustering label relaxing and label discretization with k-means. Such common practice has three disadvantages: 1) the predefined similarity graph is often fixed and may not be optimal for the subsequent clustering.
Xiaobai Liu, Lei Zhu, Jingjing Li
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Discrete Signal Processing on Graphs [PDF]
IEEE Transactions on Signal Processing, 2013 In social settings, individuals interact through webs of relationships. Each individual is a node in a complex network (or graph) of interdependencies and generates data, lots of data. We label the data by its source, or formally stated, we index the data by the nodes of the graph.
José M. F. Moura, Aliaksei Sandryhaila
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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 ...
José M. F. Moura, Aliaksei Sandryhaila
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Discrete Multi-Graph Clustering
IEEE Transactions on Image Processing, 2019Spectral clustering plays a significant role in applications that rely on multi-view data due to its well-defined mathematical framework and excellent performance on arbitrarily-shaped clusters. Unfortunately, directly optimizing the spectral clustering inevitably results in an NP-hard problem due to the discrete constraints on the clustering labels ...
Minnan Luo +5 more
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Fast Construction of Discrete Geodesic Graphs
ACM Transactions on Graphics, 2020This paper develops a new method for constructing Discrete Geodesic Graph (DGG)—an undirected, sparse graph for computing discrete geodesic distances and paths on triangle meshes. Based on a novel accuracy aware window propagation scheme, our method is able to compute the graph edges in a direct and efficient manner.
Ying He, Zheng Fang
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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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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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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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