A Survey on Metric Learning for Feature Vectors and Structured Data

The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult. This has led to the emergence of metric learning, which aims at automatically learning a metric from data and has attracted a lot of interest in machine learning and related fields for the past ten years. This survey paper proposes a systematic review of the metric learning literature, highlighting the pros and cons of each approach. We pay particular attention to Mahalanobis distance metric learning, a well-studied and successful framework, but additionally present a wide range of methods that have recently emerged as powerful alternatives, including nonlinear metric learning, similarity learning and local metric learning. Recent trends and extensions, such as semi-supervised metric learning, metric learning for histogram data and the derivation of generalization guarantees, are also covered. Finally, this survey addresses metric learning for structured data, in particular edit distance learning, and attempts to give an overview of the remaining challenges in metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new method

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

Assessing the effectiveness of multi-touch interfaces for DP operation

Navigating a vessel using dynamic positioning (DP) systems close to offshore installations is a challenge. The operator's only possibility of manipulating the system is through its interface, which can be categorized as the physical appearance of the equipment and the visualization of the system. Are there possibilities of interaction between the operator and the system that can reduce strain and cognitive load during DP operations? Can parts of the system (e.g. displays) be physically brought closer to the user to enhance the feeling of control when operating the system? Can these changes make DP operations more efficient and safe? These questions inspired this research project, which investigates the use of multi-touch and hand gestures known from consumer products to directly manipulate the visualization of a vessel in the 3D scene of a DP system. Usability methodologies and evaluation techniques that are widely used in consumer market research were used to investigate how these interaction techniques, which are new to the maritime domain, could make interaction with the DP system more efficient and transparent both during standard and safety-critical operations. After investigating which gestures felt natural to use by running user tests with a paper prototype, the gestures were implemented into a Rolls-Royce DP system and tested in a static environment. The results showed that the test participants performed significantly faster using direct gesture manipulation compared to using traditional button/menu interaction. To support the results from these tests, further tests were carried out. The purpose is to investigate how gestures are performed in a moving environment, using a motion platform to simulate rough sea conditions. The key results and lessons learned from a collection of four user experiments, together with a discussion of the choice of evaluation techniques will be discussed in this paper

Improving Entity Retrieval on Structured Data

The increasing amount of data on the Web, in particular of Linked Data, has led to a diverse landscape of datasets, which make entity retrieval a challenging task. Explicit cross-dataset links, for instance to indicate co-references or related entities can significantly improve entity retrieval. However, only a small fraction of entities are interlinked through explicit statements. In this paper, we propose a two-fold entity retrieval approach. In a first, offline preprocessing step, we cluster entities based on the \emph{x--means} and \emph{spectral} clustering algorithms. In the second step, we propose an optimized retrieval model which takes advantage of our precomputed clusters. For a given set of entities retrieved by the BM25F retrieval approach and a given user query, we further expand the result set with relevant entities by considering features of the queries, entities and the precomputed clusters. Finally, we re-rank the expanded result set with respect to the relevance to the query. We perform a thorough experimental evaluation on the Billions Triple Challenge (BTC12) dataset. The proposed approach shows significant improvements compared to the baseline and state of the art approaches