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. In addition, many research studies have focused on the use of Graphics Processing Units (GPUs) to improve the training time for machine learning algorithms. In this study, the use of an alternative platform, called High Performance Computing Cluster (HPCC), to handle unsupervised feature learning, image and speech classification and improve the computational cost is proposed. HPCC is a Big Data processing and massively parallel processing (MPP) computing platform used for solving Big Data problems. Algorithms are implemented in HPCC 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. In this study, various databases are explored, such as the CALTECH-101 and AR databases, and a subset of wild PubFig83 data to which multimedia content is added. Unsupervised learning algorithms are applied to extract low-level image features from unlabeled data using HPCC. A new object identification framework that works in a multimodal learning and classification process is proposed. Coates et al. discovered that K-Means clustering method out-performed various deep learning methods such as sparse autoencoder for image classification. K-Means implemented in HPCC with various classifiers is compared with Coates et al. classification results. Detailed results on image classification in HPCC using Naive Bayes, Random Forest, and C4.5 Decision Tree are performed and presented. The highest recognition rates are achieved using C4.5 Decision Tree classifier in HPCC systems. For example, the classification accuracy result of Coates et al. is improved from 74.3% to 85.2% using C4.5 Decision Tree classifier in HPCC. It is observed that the deeper the decision tree, the fitter the model, resulting in a higher accuracy. The most important contribution of this study is the exploration of image classification problems in HPCC platform

Deep Divergence-Based Approach to Clustering

A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function. As opposed to supervised deep learning, this line of research is in its infancy, and how to design and optimize suitable loss functions to train deep neural networks for clustering is still an open question. Our contribution to this emerging field is a new deep clustering network that leverages the discriminative power of information-theoretic divergence measures, which have been shown to be effective in traditional clustering. We propose a novel loss function that incorporates geometric regularization constraints, thus avoiding degenerate structures of the resulting clustering partition. Experiments on synthetic benchmarks and real datasets show that the proposed network achieves competitive performance with respect to other state-of-the-art methods, scales well to large datasets, and does not require pre-training steps

Supervised cross-modal factor analysis for multiple modal data classification

In this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of data, i.e., an image and a text. Cross-modal factor analysis (CFA) has been proposed to project the two different modals of data to a shared data space, so that the classification of a image or a text can be performed directly in this space. A disadvantage of CFA is that it has ignored the supervision information. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor in the shared space to use the class label information. The factor analysis parameter and the predictor parameter are learned jointly by solving one single objective function. With this objective function, we minimize the distance between the projections of image and text of the same document, and the classification error of the projection measured by hinge loss function. The objective function is optimized by an alternate optimization strategy in an iterative algorithm. Experiments in two different multiple modal document data sets show the advantage of the proposed algorithm over other CFA methods