Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Spectral Clustering Using Autoencoders and Landmarks   In this paper, we introduce an algorithm for performing spectral clustering
efficiently. Spectral clustering is a powerful clustering algorithm that
suffers from high computational complexity, due to eigen decomposition. In this
work, we first build the adjacency matrix of the corresponding graph of the
dataset. To build this matrix, we only consider a limited number of points,
called landmarks, and compute the similarity of all data points with the
landmarks. Then, we present a definition of the Laplacian matrix of the graph
that enable us to perform eigen decomposition efficiently, using a deep
autoencoder. The overall complexity of the algorithm for eigen decomposition is
$O(np)$, where $n$ is the number of data points and $p$ is the number of
landmarks. At last, we evaluate the performance of the algorithm in different
experiments.

24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin