An Analysis of Machine- and Human-Analytics in Classification

In this work, we present a study that traces the technical and cognitive processes in two visual analytics applications to a common theoretic model of soft knowledge that amy be added into a visual analytics process for constructing a decision-tree model. Both case studies involved the development of classification models based on the "bag of features" approach. Both compared a visual analytics approach using parallel coordinates with a machine-learning approach using information theory. Both found that the visual analytics approach had some advantages over the machine learning approach, especially when sparse datasets were used as the ground truth. We examine various possible factors that may have contributed to such advantages, and collect empirical evidence for supporting the observation and reasoning of these factors. We propose an information-theoretic model as a common theoretic basis to explain the phenomena exhibited in these two case studies. Together we provide interconnected empirical and theoretical evidence to support the usefulness of visual analytics

Visualization-based Active Learning for the Annotation of SAR images

Active learning has gained a high amount of attention due to its ability to label a vast amount of unlabeled collected earth observation (EO) data. In this paper, we propose a novel active learning algorithm which is mainly based on employing a low-rank classifier as the training model and introducing a visualization support data point selection, namely, first certain wrong labeled (FCWL). The training model is composed of the logistic regression loss function and the trace-norm of learning parameters as regularizer. FCWL selects those data points whose labels are predicted wrong but the classifier is highly certain about them. Our experimental results performed on different extracted features from a dataset of SAR images confirm at least 10% improvement over the state-of-the-art methods