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Krejov and Bowden [11] presented a system using a weighted graph and depth information of the hand for determining the geodesic maxima of the surface.
Jiang et al. [6] applied the domain-adaptive semantic diffusion (DASD) method [13] to MED, where the labels of the concepts related to the target events were manually annotated to positive clips for MED events and the relationship was modelled using a weighted graph.
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Using a weighted-graph representation in the compositional space, we are able to construct a regularization matrix, being the modified Laplacian operator of the weighted-graph, and use it in the DOT image reconstruction.
The alternative would have been to use a weighted graph-Laplacian [see, e.g, Chung and Taylor, 2004] that incorporates the distance between vertices.
The distances were used to construct a weighted graph of all terms provided by the GO.
In this approach, pairwise sequence similarity information for a set of sequences is used to construct a weighted graph, which is then converted into a Markov matrix.
We summarize all the graphs in D using an edge weighted graph.
The pairwise similarity between any two groups is shown in Figure 2. Using this, one can construct a weighted graph where each vertex denotes a group and the weight of each edge equals to the similarity between two groups that are connected by this edge.
CIS iteratively optimizes a grouping of vertexes in a weighted graph, using a density metric computed based on the input and output weights of a candidate cluster.
Thus, for a weighted graph, using Equation 1 may produce a reducible matrix, since some edges may have zero betweeness.
Isometric feature mapping (Isomap) [ 44], another low-dimensional embedding method, is widely used by incorporating geodesic distances on a weighted graph with metric multidimensional scaling.
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