Visual exploration of high-dimensional real-valued datasets is a fundamental task in exploratory data analysis (EDA). Existing methods use predefined criteria to choose the representation of data. There is a lack of methods that (i) elicit from the user what she has learned from the data and (ii) show patterns that she does not know yet. We construct a theoretical model where identified patterns can be input as knowledge to the system. The knowledge syntax here is intuitive, such as "this set of points forms a cluster", and requires no knowledge of maths. This background knowledge is used to find a Maximum Entropy distribution of the data, after which the system provides the user data projections in which the data and the Maximum Entropy distribution differ the most, hence showing the user aspects of the data that are maximally informative given the user's current knowledge. We provide an open source EDA system with tailored interactive visualizations to demonstrate these concepts. We study the performance of the system and present use cases on both synthetic and real data. We find that the model and the prototype system allow the user to learn information efficiently from various data sources and the system works sufficiently fast in practice. We conclude that the information theoretic approach to exploratory data analysis where patterns observed by a user are formalized as constraints provides a principled, intuitive, and efficient basis for constructing an EDA system.
Many other special-purpose methods have been developed for active learning in diverse settings, e.g., in classification and ranking, as well as explicit models for user preferences. However, as these approaches are not targeted at data exploration, we do not review them here. Finally, several special-purpose methods have been developed for visual iterative data exploration in specific contexts, e.g., for itemset mining and subgroup discovery @cite_8 @cite_26 @cite_3 @cite_13 , information retrieval @cite_7 , and network analysis @cite_12 .
@cite_26 @cite_7 @cite_8 @cite_3 @cite_13 @cite_12
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Recently active learning has attracted a lot of attention in computer vision field, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis. Most existing active learning approaches employed in computer vision adopt most uncertainty measures as instance selection criteria. Although most uncertainty query selection strategies are very effective in many circumstances, they fail to take information in the large amount of unlabeled instances into account and are prone to querying outliers. In this paper, we present a novel adaptive active learning approach that combines an information density measure and a most uncertainty measure together to select critical instances to label for image classifications. Our experiments on two essential tasks of computer vision, object recognition and scene recognition, demonstrate the efficacy of the proposed approach. As a way to relieve the tedious work of manual annotation, active learning plays important roles in many applications of visual concept recognition. In typical active learning scenarios, the number of labelled data in the seed set is usually small. However, most existing active learning algorithms only exploit the labelled data, which often suffers from over-fitting due to the small number of labelled examples. Besides, while much progress has been made in binary class active learning, little research attention has been focused on multi-class active learning. In this paper, we propose a semi-supervised batch mode multi-class active learning algorithm for visual concept recognition. Our algorithm exploits the whole active pool to evaluate the uncertainty of the data. Considering that uncertain data are always similar to each other, we propose to make the selected data as diverse as possible, for which we explicitly impose a diversity constraint on the objective function. As a multi-class active learning algorithm, our algorithm is able to exploit uncertainty across multiple classes. An efficient algorithm is used to optimize the objective function. Extensive experiments on action recognition, object classification, scene recognition, and event detection demonstrate its advantages. For learning problems where human supervision is expensive, active query selection methods are often exploited to maximise the return of each supervision. Two problems where this has been successfully applied are active discovery --- where the aim is to discover at least one instance of each rare class with few supervisions; and active learning --- where the aim is to maximise a classifier's performance with least supervision. Recently, there has been interest in optimising these tasks jointly, i.e., active learning with undiscovered classes, to support efficient interactive modelling of new domains. Mixtures of active discovery and learning and other schemes have been exploited, but perform poorly due to heuristic objectives. In this study, we show with systematic theoretical analysis how the previously disparate tasks of active discovery and learning can be cleanly unified into a single problem, and hence are able for the first time to develop a unified query algorithm to directly optimise this problem. The result is a model which consistently outperforms previous attempts at active learning in the presence of undiscovered classes, with no need to tune parameters for different datasets. It is known that productive pattern discovery from data has to interactively involve the user as directly as possible. State-of-the-art toolboxes require the specification of sophisticated workflows with an explicit selection of a data mining method, all its required parameters, and a corresponding algorithm. This hinders the desired rapid interaction---especially with users that are experts of the data domain rather than data mining experts. In this paper, we present a fundamentally new approach towards user involvement that relies exclusively on the implicit feedback available from the natural analysis behavior of the user, and at the same time allows the user to work with a multitude of pattern classes and discovery algorithms simultaneously without even knowing the details of each algorithm. To achieve this goal, we are relying on a recently proposed co-active learning model and a special feature representation of patterns to arrive at an adaptively tuned user interestingness model. At the same time, we propose an adaptive time-allocation strategy to distribute computation time among a set of underlying mining algorithms. We describe the technical details of our approach, present the user interface for gathering implicit feedback, and provide preliminary evaluation results.