Cloud based unsupervised learning architecture based on Mirroring Neural Networks

摘要

In this paper we build upon the Mirroring theorem introduced in [15] as a new method of unsupervised hierarchical pattern classification. The Mirroring theorem affirms that "given a collection of samples with enough information in it such that it can be classified into classes and sub-classes then 1. There exists a mapping which classifies and sub-classifies these samples 2. There exists a hierarchical classifier which can be constructed by using Mirroring Neural Networks (MNNs) in combination with a clustering algorithm that can approximate this mapping." This paper visualizes a cloud based scalable self learning engine, Pioneer, on top of the mirroring neural network architecture. Specifically we discuss about: 1. The modularity and scalability of MNNs to lend themselves to a cloud based architecture. 2. Validation methodology adopted to validate the parallelizing of Mirroring theorem 3. Exposing Pioneer through web service APIs to allow people to build their own unsupervised systems and allow the crowd sourcing of intelligence.