Considering the labeled samples may be difficult to obtain because they require human
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …

Low rank approximation of matrices has been well studied in literature. Singular value
decomposition, QR decomposition with column pivoting, rank revealing QR factorization …

Global machine learning force fields, with the capacity to capture collective interactions in
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …

Identification of the cis-regulatory elements controlling cell-type specific gene expression
patterns is essential for understanding the origin of cellular diversity. Conventional assays to …

This survey describes probabilistic algorithms for linear algebraic computations, such as
factorizing matrices and solving linear systems. It focuses on techniques that have a proven …

In malicious URLs detection, traditional classifiers are challenged because the data volume
is huge, patterns are changing over time, and the correlations among features are …

We study Nyström type subsampling approaches to large scale kernel methods, and prove
learning bounds in the statistical learning setting, where random sampling and high …

This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …

We introduce a new family of positive-definite kernel functions that mimic the computation in
large, multilayer neural nets. These kernel functions can be used in shallow architectures …

We reconsider randomized algorithms for the low-rank approximation of SPSD matrices
such as Laplacian and kernel matrices that arise in data analysis and machine learning …