9,480 research outputs found
Practical sketching algorithms for low-rank matrix approximation
This paper describes a suite of algorithms for constructing low-rank
approximations of an input matrix from a random linear image of the matrix,
called a sketch. These methods can preserve structural properties of the input
matrix, such as positive-semidefiniteness, and they can produce approximations
with a user-specified rank. The algorithms are simple, accurate, numerically
stable, and provably correct. Moreover, each method is accompanied by an
informative error bound that allows users to select parameters a priori to
achieve a given approximation quality. These claims are supported by numerical
experiments with real and synthetic data
Revisiting the Nystrom Method for Improved Large-Scale Machine Learning
We reconsider randomized algorithms for the low-rank approximation of
symmetric positive semi-definite (SPSD) matrices such as Laplacian and kernel
matrices that arise in data analysis and machine learning applications. Our
main results consist of an empirical evaluation of the performance quality and
running time of sampling and projection methods on a diverse suite of SPSD
matrices. Our results highlight complementary aspects of sampling versus
projection methods; they characterize the effects of common data preprocessing
steps on the performance of these algorithms; and they point to important
differences between uniform sampling and nonuniform sampling methods based on
leverage scores. In addition, our empirical results illustrate that existing
theory is so weak that it does not provide even a qualitative guide to
practice. Thus, we complement our empirical results with a suite of worst-case
theoretical bounds for both random sampling and random projection methods.
These bounds are qualitatively superior to existing bounds---e.g. improved
additive-error bounds for spectral and Frobenius norm error and relative-error
bounds for trace norm error---and they point to future directions to make these
algorithms useful in even larger-scale machine learning applications.Comment: 60 pages, 15 color figures; updated proof of Frobenius norm bounds,
added comparison to projection-based low-rank approximations, and an analysis
of the power method applied to SPSD sketche
Network Sketching: Exploiting Binary Structure in Deep CNNs
Convolutional neural networks (CNNs) with deep architectures have
substantially advanced the state-of-the-art in computer vision tasks. However,
deep networks are typically resource-intensive and thus difficult to be
deployed on mobile devices. Recently, CNNs with binary weights have shown
compelling efficiency to the community, whereas the accuracy of such models is
usually unsatisfactory in practice. In this paper, we introduce network
sketching as a novel technique of pursuing binary-weight CNNs, targeting at
more faithful inference and better trade-off for practical applications. Our
basic idea is to exploit binary structure directly in pre-trained filter banks
and produce binary-weight models via tensor expansion. The whole process can be
treated as a coarse-to-fine model approximation, akin to the pencil drawing
steps of outlining and shading. To further speedup the generated models, namely
the sketches, we also propose an associative implementation of binary tensor
convolutions. Experimental results demonstrate that a proper sketch of AlexNet
(or ResNet) outperforms the existing binary-weight models by large margins on
the ImageNet large scale classification task, while the committed memory for
network parameters only exceeds a little.Comment: To appear in CVPR201
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data
Several important applications, such as streaming PCA and semidefinite
programming, involve a large-scale positive-semidefinite (psd) matrix that is
presented as a sequence of linear updates. Because of storage limitations, it
may only be possible to retain a sketch of the psd matrix. This paper develops
a new algorithm for fixed-rank psd approximation from a sketch. The approach
combines the Nystrom approximation with a novel mechanism for rank truncation.
Theoretical analysis establishes that the proposed method can achieve any
prescribed relative error in the Schatten 1-norm and that it exploits the
spectral decay of the input matrix. Computer experiments show that the proposed
method dominates alternative techniques for fixed-rank psd matrix approximation
across a wide range of examples
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