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Indefinite proximity learning: A review
Efficient learning of a data analysis task strongly depends on the data representation. Most
methods rely on (symmetric) similarity or dissimilarity representations by means of metric …
methods rely on (symmetric) similarity or dissimilarity representations by means of metric …
Kernel K-means sampling for Nyström approximation
A fundamental problem in Nyström-based kernel matrix approximation is the sampling
method by which training set is built. In this paper, we suggest to use kernel k-means …
method by which training set is built. In this paper, we suggest to use kernel k-means …
The continuous hint factory-providing hints in vast and sparsely populated edit distance spaces
Scalable learning in reproducing kernel Krein spaces
We provide the first mathematically complete derivation of the Nystr {ö} m method for low-
rank approximation of indefinite kernels and propose an efficient method for finding an …
rank approximation of indefinite kernels and propose an efficient method for finding an …
Randomized low-rank approximation for symmetric indefinite matrices
The Nyström method is a popular choice for finding a low-rank approximation to a symmetric
positive semidefinite matrix. The method can fail when applied to symmetric indefinite …
positive semidefinite matrix. The method can fail when applied to symmetric indefinite …
Embedding and trajectories of temporal networks
Temporal network data are increasingly available in various domains, and often represent
highly complex systems with intricate structural and temporal evolutions. Due to the difficulty …
highly complex systems with intricate structural and temporal evolutions. Due to the difficulty …