Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Low-rank Metric Learning for Large-scale and High-dimensional Data   Low-rank metric learning aims to learn better discrimination of data subject
to low-rank constraints. It keeps the intrinsic low-rank structure of datasets
and reduces the time cost and memory usage in metric learning. However, it is
still a challenge for current methods to handle datasets with both high
dimensions and large numbers of samples. To address this issue, we present a
novel fast low-rank metric learning (FLRML) method.FLRML casts the low-rank
metric learning problem into an unconstrained optimization on the Stiefel
manifold, which can be efficiently solved by searching along the descent curves
of the manifold.FLRML significantly reduces the complexity and memory usage in
optimization, which makes the method scalable to both high dimensions and large
numbers of samples.Furthermore, we introduce a mini-batch version of FLRML to
make the method scalable to larger datasets which are hard to be loaded and
decomposed in limited memory. The outperforming experimental results show that
our method is with high accuracy and much faster than the state-of-the-art
methods under several benchmarks with large numbers of high-dimensional data.
Code has been made available at https://github.com/highan911/FLRML

29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
2
The categories of this paper are: math
21
The categories of this paper are: adap-org