Sparse Greedy Matrix Approximation for Machine Learning

By A. Smola, Bernhard Schölkopf. 2000

60 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test (2014, background): “Many efforts have been made to speedup the establishment of the kernel information and accelerate the implementation of kernel techniques (Smola & Schölkopf, 2000; Williams & Seeger, 2000; Fine & K.Scheinberg, 2001; Si et al., 2014).”
Sharp analysis of low-rank kernel matrix approximations (2012, neutral): “For the task of approximating the kernel matrix, algorithms such as the incomplete Cholesky decomposition with pivoting , provide an approximate greedy algorithm with the same complexity than random subsampling (Smola and Sch¨olkopf, 2000; Fine and Scheinberg, 2001).”
Biological Sequence Modeling with Convolutional Kernel Networks (2017, positive): “A typical workaround is the Nyström approximation [44, 39], which builds an explicit low dimension mapping ψ : X → Rq for a reasonably small q, such that 〈ψ(x), ψ(x)〉Rq ' K(x,x′).”
Invariance and Stability of Deep Convolutional Representations (2017, unknown): “Typically, each RKHS’s mapping is approximated by performing a projection onto a subspace of finite dimension, a classical approach to make kernel methods work at large scale [12, 31, 34].”
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations (2017, unknown): “Typically, each RKHS’s mapping is approximated by performing a projection onto a subspace of finite dimension, a classical approach to make kernel methods work at large scale [16, 41, 45].”
Kernel independent component analysis (2003, background): “2.2 Reproducing kernel Hilbert spaces Let K(x; y) be a Mercer kernel ( Saitoh, 1988 ) on X = Saitoh, 1988 ).”
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) (2005, unknown): “Smola and Schölkopf [2000] suggest a greedy algorithm to choose points to include into the active set so as to minimize the error criterion.”