Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Orthogonal Random Features   We present an intriguing discovery related to Random Fourier Features: in
Gaussian kernel approximation, replacing the random Gaussian matrix by a
properly scaled random orthogonal matrix significantly decreases kernel
approximation error. We call this technique Orthogonal Random Features (ORF),
and provide theoretical and empirical justification for this behavior.
Motivated by this discovery, we further propose Structured Orthogonal Random
Features (SORF), which uses a class of structured discrete orthogonal matrices
to speed up the computation. The method reduces the time cost from
$\mathcal{O}(d^2)$ to $\mathcal{O}(d \log d)$, where $d$ is the data
dimensionality, with almost no compromise in kernel approximation quality
compared to ORF. Experiments on several datasets verify the effectiveness of
ORF and SORF over the existing methods. We also provide discussions on using
the same type of discrete orthogonal structure for a broader range of
applications.

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The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
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The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org