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- David R. Hardoon, SÃ¡ndor SzedmÃ¡k, John Shawe-Taylor
- Neural Computation
- 2004

We present a general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representationâ€¦ (More)

Kernel methods make it relatively easy to define complex highdimensional feature spaces. This raises the question of how we can identify the relevant subspaces for a particular learning task. Whenâ€¦ (More)

We use kernel Canonical Correlation Analysis to learn a semantic representation of web images and their associated text. In the application we look at two approaches of retriev ing images based onlyâ€¦ (More)

- David R. Hardoon, John Shawe-Taylor
- Machine Learning
- 2010

We present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when one isâ€¦ (More)

- David R. Hardoon, Kitsuchart Pasupa
- ETRA
- 2010

In order to help users navigate an image search system, one could provide explicit information on a small set of images as to which of them are relevant or not to their task. These rankings areâ€¦ (More)

- David R. Hardoon, Janaina MourÃ£o Miranda, Michael J. Brammer, John Shawe-Taylor
- NeuroImage
- 2007

We introduce a new unsupervised fMRI analysis method based on kernel canonical correlation analysis which differs from the class of supervised learning methods (e.g., the support vector machine) thatâ€¦ (More)

- David R. Hardoon, John Shawe-Taylor
- Machine Learning
- 2008

Canonical Correlation Analysis is a technique for finding pairs of basis vectors that maximise the correlation of a set of paired variables, these pairs can be considered as two views of the sameâ€¦ (More)

In this chapter we show a novel application of string kernels: that is to the problem of recognising famous pianists from their style of playing. The characteristics of performers playing the sameâ€¦ (More)

- Tom Diethe, David R. Hardoon, John Shawe-Taylor
- ECML/PKDD
- 2010

There are many situations in which we have more than one view of a single data source, or in which we have multiple sources of data that are aligned. We would like to be able to build classifiersâ€¦ (More)

- Zakria Hussain, John Shawe-Taylor, David R. Hardoon, Charanpal Dhanjal
- IEEE Transactions on Information Theory
- 2011

We derive generalization error (loss) bounds for orthogonal matching pursuit algorithms, starting with kernel matching pursuit and sparse kernel principal components analysis. We propose (to the bestâ€¦ (More)