121 research outputs found
Learning relevant eye movement feature spaces across users
In this paper we predict the relevance of images based on a lowdimensional feature space found using several users’ eye movements. Each user is given an image-based search task, during which their eye movements are extracted using a Tobii eye tracker. The users also provide us with explicit feedback regarding the relevance of images. We demonstrate that by using a greedy Nystrom algorithm on the eye movement features of different users, we can find a suitable low-dimensional feature space for learning. We validate the suitability of this feature space by projecting the eye movement features of a new user into this space, training an online learning algorithm using these features, and showing that the number of mistakes (regret over time) made in predicting relevant images is lower than when using the original eye movement features. We also plot Recall-Precision and ROC curves, and use a sign test to verify the statistical significance of our results
Less is More: Nystr\"om Computational Regularization
We study Nystr\"om type subsampling approaches to large scale kernel methods,
and prove learning bounds in the statistical learning setting, where random
sampling and high probability estimates are considered. In particular, we prove
that these approaches can achieve optimal learning bounds, provided the
subsampling level is suitably chosen. These results suggest a simple
incremental variant of Nystr\"om Kernel Regularized Least Squares, where the
subsampling level implements a form of computational regularization, in the
sense that it controls at the same time regularization and computations.
Extensive experimental analysis shows that the considered approach achieves
state of the art performances on benchmark large scale datasets.Comment: updated version of NIPS 2015 (oral
On landmark selection and sampling in high-dimensional data analysis
In recent years, the spectral analysis of appropriately defined kernel
matrices has emerged as a principled way to extract the low-dimensional
structure often prevalent in high-dimensional data. Here we provide an
introduction to spectral methods for linear and nonlinear dimension reduction,
emphasizing ways to overcome the computational limitations currently faced by
practitioners with massive datasets. In particular, a data subsampling or
landmark selection process is often employed to construct a kernel based on
partial information, followed by an approximate spectral analysis termed the
Nystrom extension. We provide a quantitative framework to analyse this
procedure, and use it to demonstrate algorithmic performance bounds on a range
of practical approaches designed to optimize the landmark selection process. We
compare the practical implications of these bounds by way of real-world
examples drawn from the field of computer vision, whereby low-dimensional
manifold structure is shown to emerge from high-dimensional video data streams.Comment: 18 pages, 6 figures, submitted for publicatio
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