Proceedings ArticleDOI
Greedy Gaussian Process Regression Applied to Object Categorization and Regression
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TLDR
This work proposes an approximation of Gaussian Process and applies it to Classification and Regression tasks using a greedy approach to subset selection and the inducing input choice to approximate the kernel matrix, resulting in faster retrieval timings.Abstract:
In this work we propose an approximation of Gaussian Process and apply it to Classification and Regression tasks. We, primarily, target the problem of visual object categorization using a Greedy variant of Gaussian Processes. To deal with the prohibitive training and inferencing cost of GP, we devise a greedy approach to subset selection and the inducing input choice to approximate the kernel matrix, resulting in faster retrieval timings. A localized combination of kernel functions is designed and used in a framework of sparse approximations to Gaussian Processes for visual object categorization and generic regression tasks. Through exhaustive experimentation and empirical results we demonstrate the effectiveness of the proposed approach, when compared with other kernel based methods.read more
Citations
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Journal ArticleDOI
Fixed-budget approximation of the inverse kernel matrix for identification of nonlinear dynamic processes
N. R. Antropov,Evgeny Agafonov,Vadim Tynchenko,Vladimir Bukhtoyarov,Vladislav Viktorovich Kukartsev +4 more
TL;DR: A new regularized kernel least squares algorithm based on the fixed-budget approximation of the kernel matrix that allows regulating the computational burden of the identification algorithm and obtaining the least approximation error.
References
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Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
TL;DR: The incremental algorithm is compared experimentally to an earlier batch Bayesian algorithm, as well as to one based on maximum-likelihood, which have comparable classification performance on small training sets, but incremental learning is significantly faster, making real-time learning feasible.
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A Unifying View of Sparse Approximate Gaussian Process Regression
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Proceedings Article
Sparse Gaussian Processes using Pseudo-inputs
Edward Snelson,Zoubin Ghahramani +1 more
TL;DR: It is shown that this new Gaussian process (GP) regression model can match full GP performance with small M, i.e. very sparse solutions, and it significantly outperforms other approaches in this regime.
Proceedings ArticleDOI
The pyramid match kernel: discriminative classification with sets of image features
Kristen Grauman,Trevor Darrell +1 more
TL;DR: A new fast kernel function is presented which maps unordered feature sets to multi-resolution histograms and computes a weighted histogram intersection in this space and is shown to be positive-definite, making it valid for use in learning algorithms whose optimal solutions are guaranteed only for Mercer kernels.
Proceedings ArticleDOI
Representing shape with a spatial pyramid kernel
TL;DR: This work introduces a descriptor that represents local image shape and its spatial layout, together with a spatial pyramid kernel that is designed so that the shape correspondence between two images can be measured by the distance between their descriptors using the kernel.