Indefinite proximity learning: A review

FM Schleif, P Tino - Neural computation, 2015 - ieeexplore.ieee.org
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 …

Kernel K-means sampling for Nyström approximation

L He, H Zhang - IEEE Transactions on Image Processing, 2018 - ieeexplore.ieee.org
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 …

The continuous hint factory-providing hints in vast and sparsely populated edit distance spaces

B Paaßen, B Hammer, TW Price, T Barnes… - ar** python programs to vectors using recursive neural encodings
B Paassen, J McBroom… - Journal of …, 2021 - jedm.educationaldatamining.org
Educational data mining involves the application of data mining techniques to student
activity. However, in the context of computer programming, many data mining techniques …

Scalable learning in reproducing kernel Krein spaces

D Oglic, T Gärtner - International Conference on Machine …, 2019 - proceedings.mlr.press
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 …

Randomized low-rank approximation for symmetric indefinite matrices

Y Nakatsukasa, T Park - SIAM Journal on Matrix Analysis and Applications, 2023 - SIAM
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 …

Embedding and trajectories of temporal networks

C Thongprayoon, L Livi, N Masuda - IEEE Access, 2023 - ieeexplore.ieee.org
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 …