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Next we focus on the proof of (iii) based on spectral graph theory.
Motivated from geometric learning, we describe a method based on spectral graph theory.
Motivated from geometric learning, new approaches [16] [18] based on spectral graph theory [19] have been recently proposed to summarize population structure.
By exploiting the properties of kernel methods, relations between genes with similar functions but active in alternative pathways could be incorporated in a support vector machine classifier based on spectral graph theory.
A similar approach based on spectral graph theory is also treated by Lee et al. [20] with a nice illustration on the POPRES data [21], but with different choices of weight and data renormalization (see Methods and Discussion).
For this, we proposed a method based on spectral graph theory and introduced the concept of -graph, for a subgroup of the icosahedral group.
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IsoRankN is a global multiple network alignment based on spectral clustering on the induced graph of pairwise alignment scores.
Results: We introduce IsoRankN (IsoRank-Nibble) a global multiple-network alignment tool based on spectral clustering on the induced graph of pairwise alignment scores.
Based on spectral clustering on the induced graph of pairwise alignment scores, our program IsoRankN automatically handles noisy and incomplete input data.
Recently, a prominent alternative class of graph embedding methods based on spectral clustering has been used [12].
Zahedi, R. & Khansari, M. A new immunization algorithm based on spectral properties for complex networks.
More suggestions(15)
based on spectral subtraction
based on spectral splitting
based on bipartite graph
based on spectral shape
based on spectral partitioning
based on approximate graph
based on spectral clustering
based on spectral classification
based on spectral reflectance
based on spectral decomposition
based on spectral substraction
based on spectral filtering
based on spectral envelope
based on spectral emissivity
based on spectral estimation
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