Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recklessly Approximate Sparse Coding   It has recently been observed that certain extremely simple feature encoding
techniques are able to achieve state of the art performance on several standard
image classification benchmarks including deep belief networks, convolutional
nets, factored RBMs, mcRBMs, convolutional RBMs, sparse autoencoders and
several others. Moreover, these "triangle" or "soft threshold" encodings are
ex- tremely efficient to compute. Several intuitive arguments have been put
forward to explain this remarkable performance, yet no mathematical
justification has been offered.
  The main result of this report is to show that these features are realized as
an approximate solution to the a non-negative sparse coding problem. Using this
connection we describe several variants of the soft threshold features and
demonstrate their effectiveness on two image classification benchmark tasks.

25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex