With the success of local features in object recognition, feature-set representations are
widely used in computer vision and related domains. Pyramid match kernel (PMK) is an …

In numerous domains it is useful to represent a single example by the set of the local
features or parts that comprise it. However, this representation poses a challenge to many …

Pyramid intersection is an efficient method for computing an approximate partial matching
between two sets of feature vectors. We introduce a novel pyramid embedding based on a …

Matching local visual features is a crucial problem in computer vision and its accuracy
greatly depends on the choice of similarity measure. As it is generally very difficult to design …

Kernel-based feature combination techniques such as Multiple Kernel Learning use
arithmetical operations to linearly combine different kernels. We have observed that the …

It is often useful to represent a single example by a set of the local features that comprise it.
However, this representation poses a challenge to many conventional learning techniques …

Discriminative learning is challenging when examples are sets of features, and the sets vary
in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods …

Kernel approximation using randomized feature maps has recently gained a lot of interest. In
this work, we identify that previous approaches for polynomial kernel approximation create …

As the commonly used representation of a feature-rich data object has evolved from a single
feature vector to a set of feature vectors, a key challenge in building a content-based search …

Kernel matching pursuit is a greedy algorithm for building an approximation of a discriminant
function as a linear combination of some basis functions selected from a kernel-induced …