Paper

Sparse kernel maps and faster product quantization learning. We propose sparse feature map representations of kernels similar to sparse expansions such as matching pursuit, linking previous work on dense and sparse explicit feature maps. The sparse maps can be smaller and faster in certain cases. One of these is Product Quantisation (PQ), that we reinterpret as a sparse kernel encoding. By doing so, we show for the first time that PQ can accelerate learning in addition to compressing the data.

ICCV 2011 Poster

Paper

Learning Equivariant Structured Output SVM Regressors. We introcude a method to learn equivariant functions with Supprot Vector Machines (SVMs). Examples include: a transformation-invariant multi-class classifier, learning to detect a rotating object without searching for the rotation, and learning to rank images of pedestrians invariantly to jitter and articulation.

CVPR 2010 Poster

Paper

Efficient additive kernels: The homogeneous kernel map. We introduce closed-form finite dimensional feature maps approximating the additive kernels (intersection, Hellinger’s, χ2, Jensen-Shannon, ...). By adding onle line to your code you can use non-linear additive kernels as if they were linear, with vastly improved training and testing speed and compactness of the resulting models (code).