Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generative approach to unsupervised deep local learning   Most existing feature learning methods optimize inflexible handcrafted
features and the affinity matrix is constructed by shallow linear embedding
methods. Different from these conventional methods, we pretrain a generative
neural network by stacking convolutional autoencoders to learn the latent data
representation and then construct an affinity graph with them as a prior. Based
on the pretrained model and the constructed graph, we add a self-expressive
layer to complete the generative model and then fine-tune it with a new loss
function, including the reconstruction loss and a deliberately defined
locality-preserving loss. The locality-preserving loss designed by the
constructed affinity graph serves as prior to preserve the local structure
during the fine-tuning stage, which in turn improves the quality of feature
representation effectively. Furthermore, the self-expressive layer between the
encoder and decoder is based on the assumption that each latent feature is a
linear combination of other latent features, so the weighted combination
coefficients of the self-expressive layer are used to construct a new refined
affinity graph for representing the data structure. We conduct experiments on
four datasets to demonstrate the superiority of the representation ability of
our proposed model over the state-of-the-art methods.

15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph