Semi-Automatic Data Annotation guided by Feature Space Projection

Data annotation using visual inspection (supervision) of each training sample can be laborious. Interactive solutions alleviate this by helping experts propagate labels from a few supervised samples to unlabeled ones based solely on the visual analysis of their feature space projection (with no further sample supervision). We present a semi-automatic data annotation approach based on suitable feature space projection and semi-supervised label estimation. We validate our method on the popular MNIST dataset and on images of human intestinal parasites with and without fecal impurities, a large and diverse dataset that makes classification very hard. We evaluate two approaches for semi-supervised learning from the latent and projection spaces, to choose the one that best reduces user annotation effort and also increases classification accuracy on unseen data. Our results demonstrate the added-value of visual analytics tools that combine complementary abilities of humans and machines for more effective machine learning.Comment: 28 pages, 10 figure

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction

Applying dimensionality reduction (DR) to large, high-dimensional data sets can be challenging when distinguishing the underlying high-dimensional data clusters in a 2D projection for exploratory analysis. We address this problem by first sharpening the clusters in the original high-dimensional data prior to the DR step using Local Gradient Clustering (LGC). We then project the sharpened data from the high-dimensional space to 2D by a user-selected DR method. The sharpening step aids this method to preserve cluster separation in the resulting 2D projection. With our method, end-users can label each distinct cluster to further analyze an otherwise unlabeled data set. Our `High-Dimensional Sharpened DR' (HD-SDR) method, tested on both synthetic and real-world data sets, is favorable to DR methods with poor cluster separation and yields a better visual cluster separation than these DR methods with no sharpening. Our method achieves good quality (measured by quality metrics) and scales computationally well with large high-dimensional data. To illustrate its concrete applications, we further apply HD-SDR on a recent astronomical catalog.Comment: This paper has been accepted for Information Visualization. Copyright may be transferred without notice, after which this version may no longer be accessibl

Tools of Trade of the Next Blue-Collar Job? Antecedents, Design Features, and Outcomes of Interactive Labeling Systems

Supervised machine learning is becoming increasingly popular - and so is the need for annotated training data. Such data often needs to be manually labeled by human workers, not unlikely to negatively impact the involved workforce. To alleviate this issue, a new information systems class has emerged - interactive labeling systems. However, this young, but rapidly growing field lacks guidance and structure regarding the design of such systems. Against this backdrop, this paper describes antecedents, design features, and outcomes of interactive labeling systems. We perform a systematic literature review, identifying 188 relevant articles. Our results are presented as a morphological box with 14 dimensions, which we evaluate using card sorting. By additionally offering this box as a web-based artifact, we provide actionable guidance for interactive labeling system development for scholars and practitioners. Lastly, we discuss imbalances in the article distribution of our morphological box and suggest future work directions

User-Centric Active Learning for Outlier Detection

Outlier detection searches for unusual, rare observations in large, often high-dimensional data sets. One of the fundamental challenges of outlier detection is that ``unusual\u27\u27 typically depends on the perception of a user, the recipient of the detection result. This makes finding a formal definition of ``unusual\u27\u27 that matches with user expectations difficult. One way to deal with this issue is active learning, i.e., methods that ask users to provide auxiliary information, such as class label annotations, to return algorithmic results that are more in line with the user input. Active learning is well-suited for outlier detection, and many respective methods have been proposed over the last years. However, existing methods build upon strong assumptions. One example is the assumption that users can always provide accurate feedback, regardless of how algorithmic results are presented to them -- an assumption which is unlikely to hold when data is high-dimensional. It is an open question to which extent existing assumptions are in the way of realizing active learning in practice. In this thesis, we study this question from different perspectives with a differentiated, user-centric view on active learning. In the beginning, we structure and unify the research area on active learning for outlier detection. Specifically, we present a rigorous specification of the learning setup, structure the basic building blocks, and propose novel evaluation standards. Throughout our work, this structure has turned out to be essential to select a suitable active learning method, and to assess novel contributions in this field. We then present two algorithmic contributions to make active learning for outlier detection user-centric. First, we bring together two research areas that have been looked at independently so far: outlier detection in subspaces and active learning. Subspace outlier detection are methods to improve outlier detection quality in high-dimensional data, and to make detection results more easy to interpret. Our approach combines them with active learning such that one can balance between detection quality and annotation effort. Second, we address one of the fundamental difficulties with adapting active learning to specific applications: selecting good hyperparameter values. Existing methods to estimate hyperparameter values are heuristics, and it is unclear in which settings they work well. In this thesis, we therefore propose the first principled method to estimate hyperparameter values. Our approach relies on active learning to estimate hyperparameter values, and returns a quality estimate of the values selected. In the last part of the thesis, we look at validating active learning for outlier detection practically. There, we have identified several technical and conceptual challenges which we have experienced firsthand in our research. We structure and document them, and finally derive a roadmap towards validating active learning for outlier detection with user studies