Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Streaming Coresets for Symmetric Tensor Factorization   Factorizing tensors has recently become an important optimization module in a
number of machine learning pipelines, especially in latent variable models. We
show how to do this efficiently in the streaming setting. Given a set of $n$
vectors, each in $\mathbb{R}^d$, we present algorithms to select a sublinear
number of these vectors as coreset, while guaranteeing that the CP
decomposition of the $p$-moment tensor of the coreset approximates the
corresponding decomposition of the $p$-moment tensor computed from the full
data. We introduce two novel algorithmic techniques: online filtering and
kernelization. Using these two, we present six algorithms that achieve
different tradeoffs of coreset size, update time and working space, beating or
matching various state of the art algorithms. In the case of matrices
($2$-ordered tensor), our online row sampling algorithm guarantees $(1 \pm
\epsilon)$ relative error spectral approximation. We show applications of our
algorithms in learning single topic modeling.

23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph