Composite Kernels for Hypertext Categorisation

Composite Kernels for Hypertext Categorisation

Integrating Spatial Proximity with Manifold Learning

Dimension reduction is a useful preprocessing step for many types hyperspectral image analysis, including visualization, regression, clustering and classification. By dimension reduction, high dimensional data are mapped into a lower dimensional space while the important features of the original data are preserved according to a given criterion. Although linear dimension reduction methods such ...

Improving “bag-of-keypoints” image categorisation: Generative Models and PDF-Kernels

In this paper we propose two distinct enhancements to the basic “bag-of-keypoints” image categorisation scheme proposed in [4]. In this approach images are represented as a variable sized set of local image features (keypoints). Thus, we require machine learning tools which can operate on sets of vectors. In [4] this is achieved by representing the set as a histogram over bins found by k-means....

Configural information in gender categorisation.

The role of configural information in gender categorisation was studied by aligning the top half of one face with the bottom half of another. The two faces had the same or different genders. Experiment 1 shows that participants were slower and made more errors in categorising the gender in either half of these composite faces when the two faces had a different gender, relative to control condit...

Econometric analysis of vast covariance matrices using composite realized kernels∗

We propose a composite realized kernel to estimate the ex-post covariation of asset prices. Composite realized kernels are a data efficient method where the covariance estimate is composed of univariate realized kernels to estimate variances and bivariate realized kernels to estimate correlations. We analyze the merits of our composite realized kernels in an ultra high dimensional environment, ...