2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2017
DOI: 10.1109/icassp.2017.7952672
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Cited by 70 publications
(79 citation statements)
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“…For fixed Y, (23) reduces to a quadratic program (QP) subject to linear constraints, which can be solved via interior point methods. For large graphs, scalable alternatives include the alternating-direction method of multipliers (ADMM), or, primal-dual solvers of the reformulation described in the following section; see (26). For fixed L, the resulting problem is a matrix-valued counterpart of (22).…”
Section: A Laplacian-based Factor Analysis Model and Graph Kernel Rementioning
confidence: 99%
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“…For fixed Y, (23) reduces to a quadratic program (QP) subject to linear constraints, which can be solved via interior point methods. For large graphs, scalable alternatives include the alternating-direction method of multipliers (ADMM), or, primal-dual solvers of the reformulation described in the following section; see (26). For fixed L, the resulting problem is a matrix-valued counterpart of (22).…”
Section: A Laplacian-based Factor Analysis Model and Graph Kernel Rementioning
confidence: 99%
“…to diag(W) = 0, W ij = W ji ≥ 0, i = j, one can span a host of approaches to graph inference from smooth signals. Examples include the Laplacianbased factor analysis model [9] in (26), and common graph constructions using the Gaussian kernel to define edge weights W ij := exp −…”
Section: Comparative Summarymentioning
confidence: 99%
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