Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchically Compositional Kernels for Scalable Nonparametric Learning   We propose a novel class of kernels to alleviate the high computational cost
of large-scale nonparametric learning with kernel methods. The proposed kernel
is defined based on a hierarchical partitioning of the underlying data domain,
where the Nystr\"om method (a globally low-rank approximation) is married with
a locally lossless approximation in a hierarchical fashion. The kernel
maintains (strict) positive-definiteness. The corresponding kernel matrix
admits a recursively off-diagonal low-rank structure, which allows for fast
linear algebra computations. Suppressing the factor of data dimension, the
memory and arithmetic complexities for training a regression or a classifier
are reduced from $O(n^2)$ and $O(n^3)$ to $O(nr)$ and $O(nr^2)$, respectively,
where $n$ is the number of training examples and $r$ is the rank on each level
of the hierarchy. Although other randomized approximate kernels entail a
similar complexity, empirical results show that the proposed kernel achieves a
matching performance with a smaller $r$. We demonstrate comprehensive
experiments to show the effective use of the proposed kernel on data sizes up
to the order of millions.

27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci