Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dimensionality Reduction and Reconstruction using Mirroring Neural
  Networks and Object Recognition based on Reduced Dimension Characteristic
  Vector   In this paper, we present a Mirroring Neural Network architecture to perform
non-linear dimensionality reduction and Object Recognition using a reduced
lowdimensional characteristic vector. In addition to dimensionality reduction,
the network also reconstructs (mirrors) the original high-dimensional input
vector from the reduced low-dimensional data. The Mirroring Neural Network
architecture has more number of processing elements (adalines) in the outer
layers and the least number of elements in the central layer to form a
converging-diverging shape in its configuration. Since this network is able to
reconstruct the original image from the output of the innermost layer (which
contains all the information about the input pattern), these outputs can be
used as object signature to classify patterns. The network is trained to
minimize the discrepancy between actual output and the input by back
propagating the mean squared error from the output layer to the input layer.
After successfully training the network, it can reduce the dimension of input
vectors and mirror the patterns fed to it. The Mirroring Neural Network
architecture gave very good results on various test patterns.

3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat