Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recycling Randomness with Structure for Sublinear time Kernel Expansions   We propose a scheme for recycling Gaussian random vectors into structured
matrices to approximate various kernel functions in sublinear time via random
embeddings. Our framework includes the Fastfood construction as a special case,
but also extends to Circulant, Toeplitz and Hankel matrices, and the broader
family of structured matrices that are characterized by the concept of
low-displacement rank. We introduce notions of coherence and graph-theoretic
structural constants that control the approximation quality, and prove
unbiasedness and low-variance properties of random feature maps that arise
within our framework. For the case of low-displacement matrices, we show how
the degree of structure and randomness can be controlled to reduce statistical
variance at the cost of increased computation and storage requirements.
Empirical results strongly support our theory and justify the use of a broader
family of structured matrices for scaling up kernel methods using random
features.

14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th