22,593 research outputs found
Stochastic Low-Rank Kernel Learning for Regression
We present a novel approach to learn a kernel-based regression function. It
is based on the useof conical combinations of data-based parameterized kernels
and on a new stochastic convex optimization procedure of which we establish
convergence guarantees. The overall learning procedure has the nice properties
that a) the learned conical combination is automatically designed to perform
the regression task at hand and b) the updates implicated by the optimization
procedure are quite inexpensive. In order to shed light on the appositeness of
our learning strategy, we present empirical results from experiments conducted
on various benchmark datasets.Comment: International Conference on Machine Learning (ICML'11), Bellevue
(Washington) : United States (2011
Regression on fixed-rank positive semidefinite matrices: a Riemannian approach
The paper addresses the problem of learning a regression model parameterized
by a fixed-rank positive semidefinite matrix. The focus is on the nonlinear
nature of the search space and on scalability to high-dimensional problems. The
mathematical developments rely on the theory of gradient descent algorithms
adapted to the Riemannian geometry that underlies the set of fixed-rank
positive semidefinite matrices. In contrast with previous contributions in the
literature, no restrictions are imposed on the range space of the learned
matrix. The resulting algorithms maintain a linear complexity in the problem
size and enjoy important invariance properties. We apply the proposed
algorithms to the problem of learning a distance function parameterized by a
positive semidefinite matrix. Good performance is observed on classical
benchmarks
Scalable Kernel Methods via Doubly Stochastic Gradients
The general perception is that kernel methods are not scalable, and neural
nets are the methods of choice for nonlinear learning problems. Or have we
simply not tried hard enough for kernel methods? Here we propose an approach
that scales up kernel methods using a novel concept called "doubly stochastic
functional gradients". Our approach relies on the fact that many kernel methods
can be expressed as convex optimization problems, and we solve the problems by
making two unbiased stochastic approximations to the functional gradient, one
using random training points and another using random functions associated with
the kernel, and then descending using this noisy functional gradient. We show
that a function produced by this procedure after iterations converges to
the optimal function in the reproducing kernel Hilbert space in rate ,
and achieves a generalization performance of . This doubly
stochasticity also allows us to avoid keeping the support vectors and to
implement the algorithm in a small memory footprint, which is linear in number
of iterations and independent of data dimension. Our approach can readily scale
kernel methods up to the regimes which are dominated by neural nets. We show
that our method can achieve competitive performance to neural nets in datasets
such as 8 million handwritten digits from MNIST, 2.3 million energy materials
from MolecularSpace, and 1 million photos from ImageNet.Comment: 32 pages, 22 figure
Preconditioning Kernel Matrices
The computational and storage complexity of kernel machines presents the
primary barrier to their scaling to large, modern, datasets. A common way to
tackle the scalability issue is to use the conjugate gradient algorithm, which
relieves the constraints on both storage (the kernel matrix need not be stored)
and computation (both stochastic gradients and parallelization can be used).
Even so, conjugate gradient is not without its own issues: the conditioning of
kernel matrices is often such that conjugate gradients will have poor
convergence in practice. Preconditioning is a common approach to alleviating
this issue. Here we propose preconditioned conjugate gradients for kernel
machines, and develop a broad range of preconditioners particularly useful for
kernel matrices. We describe a scalable approach to both solving kernel
machines and learning their hyperparameters. We show this approach is exact in
the limit of iterations and outperforms state-of-the-art approximations for a
given computational budget
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