  This paper describes an effective and efficient image classification
framework nominated distributed deep representation learning model (DDRL). The
aim is to strike the balance between the computational intensive deep learning
approaches (tuned parameters) which are intended for distributed computing, and
the approaches that focused on the designed parameters but often limited by
sequential computing and cannot scale up. In the evaluation of our approach, it
is shown that DDRL is able to achieve state-of-art classification accuracy
efficiently on both medium and large datasets. The result implies that our
approach is more efficient than the conventional deep learning approaches, and
can be applied to big data that is too complex for parameter designing focused
approaches. More specifically, DDRL contains two main components, i.e., feature
extraction and selection. A hierarchical distributed deep representation
learning algorithm is designed to extract image statistics and a nonlinear
mapping algorithm is used to map the inherent statistics into abstract
features. Both algorithms are carefully designed to avoid millions of
parameters tuning. This leads to a more compact solution for image
classification of big data. We note that the proposed approach is designed to
be friendly with parallel computing. It is generic and easy to be deployed to
different distributed computing resources. In the experiments, the largescale
image datasets are classified with a DDRM implementation on Hadoop MapReduce,
which shows high scalability and resilience.
