Sparse online feature maps

Online kernel methods suffer from computational and memory complexity in large-scale problems. Due to these drawbacks, budget online kernel learning and kernel approximation (low-dimensional feature map approximation) methods are widely used to speed up time and to reduce memory usage of kernel appr...

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Veröffentlicht in:	Knowledge-based systems 2018-07, Vol.151, p.62-77
Hauptverfasser:	Salehi-M., Nima, Monsefi, Reza, Sadoghi Yazdi, Hadi
Format:	Artikel
Sprache:	eng
Schlagworte:	Approximation Complexity Complexity theory Computation Computer memory Distance learning Explicit feature map Feature maps Gram-Schmidt orthogonalization process Kernel methods Mathematical analysis Online data bases Orthogonality Redundancy Regularization Single-pass online learning Sparsity Teaching methods
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