Unsupervised Learning and Image Classification in High Performance Computing Cluster

Feature learning and object classification in machine learning have become very active research areas in recent decades. Identifying good features has various benefits for object classification in respect to reducing the computational cost and increasing the classification accuracy. We propose using a multimodal learning and object identification framework with an alternative platform, called High Performance Computing Cluster (HPCC Systems®), to speed up the optimization stages and to handle data of any dimension. Our framework first learns representative bases (or centroids) over unlabeled data for each model through the K-means unsupervised learning method. Then, to extract the desired features from the labeled data, the correlation between the labeled data and representative bases is calculated. These labeled features are fused to represent the identity and then fed to the classifiers to make the final recognition. In addition, many research studies have focused on improving optimization methods and the use of Graphics Processing Units (GPUs) to improve the training time for machine learning algorithms. This study is aimed at exploring feature learning and object classification ideas in HPCC Systems platform. HPCC Systems is a Big Data processing and massively parallel processing (MPP) computing platform used for solving Big Data problems. Algorithms are implemented in HPCC Systems® 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. We evaluate our proposed framework in this new platform on various databases such as the CALTECH-101, AR databases, and a subset of wild PubFig83 data that we add multimedia content. For instance, we are able to improve on the classification accuracy result of [3] from 74.3% to 78.9% on AR database using Decision Tree C4.5 classifier.