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Hash kernels and structured learning

Shi, Qinfeng

Description

Vast amounts of data being generated, how to process massive data remains a challenge for machine learning algorithms. We propose hash kernels to facilitate efficient kernels which can deal with massive multi-class problems. We show a principled way to compute the kernel matrix for data streams and sparse feature spaces. We further generalise it via sampling to graphs. Later we exploit the connection between hash kernels with compressed sensing, and apply hashing to face recognition which...[Show more]

CollectionsOpen Access Theses
Type: Thesis (PhD)
URI: http://hdl.handle.net/1885/149746

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