Unsupervised learning and image classification in high performance computing cluster

Abstract

Feature learning and object classification in machine learning are ongoing research areas. Identifying good features has various benefits for object classification with respect to reducing the computational cost and increasing the classification accuracy. In this study, we implement a new multimodal feature learning method and object identification framework using High Performance Computing Cluster (HPCC SystemsR). The framework first learns representative weights over un-labeled data for each model through the K-means unsupervised learning method. Then, the desired features are extracted from the labeled data using the correlation between the labeled data and representative bases. These labeled features are fused and fed to the classifiers to make the final recognition. HPCC SystemsR is a Big Data processing and massively parallel processing (MPP) computing platform used for solving Big Data problems. Algorithms are implemented in HPCC SystemsR with a language called Enterprise Control Language (ECL) which is a declarative, data-centric programming language. It is a powerful, high-level, parallel programming language ideal for Big Data intensive applications. The proposed framework is evaluated using various databases such as the CALTECH-101, AR databases, and a subset of wild PubFig83 data to which multimedia content is added. For instance, the classification accuracy result of [1] is improved from 74.3 to 78.9% on AR database using Decision Tree C4.5 classifier.