Probabilistic topographic information visualisation

The focus of this thesis is the extension of topographic visualisation mappings to allow for the incorporation of uncertainty. Few visualisation algorithms in the literature are capable of mapping uncertain data with fewer able to represent observation uncertainties in visualisations. As such, modifications are made to NeuroScale, Locally Linear Embedding, Isomap and Laplacian Eigenmaps to incorporate uncertainty in the observation and visualisation spaces. The proposed mappings are then called Normally-distributed NeuroScale (N-NS), T-distributed NeuroScale (T-NS), Probabilistic LLE (PLLE), Probabilistic Isomap (PIso) and Probabilistic Weighted Neighbourhood Mapping (PWNM). These algorithms generate a probabilistic visualisation space with each latent visualised point transformed to a multivariate Gaussian or T-distribution, using a feed-forward RBF network. Two types of uncertainty are then characterised dependent on the data and mapping procedure. Data dependent uncertainty is the inherent observation uncertainty. Whereas, mapping uncertainty is defined by the Fisher Information of a visualised distribution. This indicates how well the data has been interpolated, offering a level of ‘surprise’ for each observation. These new probabilistic mappings are tested on three datasets of vectorial observations and three datasets of real world time series observations for anomaly detection. In order to visualise the time series data, a method for analysing observed signals and noise distributions, Residual Modelling, is introduced. The performance of the new algorithms on the tested datasets is compared qualitatively with the latent space generated by the Gaussian Process Latent Variable Model (GPLVM). A quantitative comparison using existing evaluation measures from the literature allows performance of each mapping function to be compared. Finally, the mapping uncertainty measure is combined with NeuroScale to build a deep learning classifier, the Cascading RBF. This new structure is tested on the MNist dataset achieving world record performance whilst avoiding the flaws seen in other Deep Learning Machines

A Decision Support System to Ease Operator Overload in Multibeam Passive Sonar

Creating human-informative signal processing systems for the underwater acoustic environment that do not generate operator cognitive saturation and overload is a major challenge. To alleviate cognitive operator overload, we present a visual analytics methodology in which multiple beam-formed sonar returns are mapped to an optimized 2-D visual representation, which preserves the relevant data structure. This representation alerts the operator as to which beams are likely to contain anomalous information by modeling a latent distribution of information for each beam. Sonar operators therefore focus their attention only on the surprising events. In addition to the principled visualization of high-dimensional uncertain data, the system quantifies anomalous information using a Fisher Information measure. Central to this process is the novel use of both signal and noise observation modeling to characterize the sensor information. A demonstration of detecting exceptionally low signal-to-noise ratio targets embedded in real-world 33-beam passive sonar data is presented

Topographic visual analytics of multibeam dynamic SONAR data

This paper considers the problem of low-dimensional visualisation of very high dimensional information sources for the purpose of situation awareness in the maritime environment. In response to the requirement for human decision support aids to reduce information overload (and specifically, data amenable to inter-point relative similarity measures) appropriate to the below-water maritime domain, we are investigating a preliminary prototype topographic visualisation model. The focus of the current paper is on the mathematical problem of exploiting a relative dissimilarity representation of signals in a visual informatics mapping model, driven by real-world sonar systems. A realistic noise model is explored and incorporated into non-linear and topographic visualisation algorithms building on the approach of [9]. Concepts are illustrated using a real world dataset of 32 hydrophones monitoring a shallow-water environment in which targets are present and dynamic

Γ-stochastic neighbour embedding for feed-forward data visualization

t-distributed Stochastic Neighbour Embedding (t-SNE) is one of the most popular nonlinear dimension reduction techniques used in multiple application domains. In this paper we propose a variation on the embedding neighbourhood distribution, resulting in Γ-SNE, which can construct a feed-forward mapping using an RBF network. We compare the visualizations generated by Γ-SNE with those of t-SNE and provide empirical evidence suggesting the network is capable of robust interpolation and automatic weight regularization

Wireless monitoring and Real-time Adaptive Predictive Indicator of Deterioration

To assist in the early warning of deterioration in hospitalised children we studied the feasibility of collecting continuous wireless physiological data using Lifetouch (ECG-derived heart and respiratory rate) and WristOx2 (pulse-oximetry and derived pulse rate) sensors. We compared our bedside paediatric early warning (PEW) score and a machine learning automated approach: a Real-time Predictive Indicator of Deterioration (RAPID) to identify children experiencing significant clinical deterioration. 982 patients contributed 7,073,486 minutes during 1263 monitoring sessions. The proportion of intended monitoring time was 93 % for Lifetouch and 55% for WristOx2. Valid clinical data was 63 % of intended monitoring time for Lifetouch and 50% WristOx2. 29 patients experienced 36 clinically significant deteriorations. RAPID Index detected significant deterioration more frequently (77 to 97%) and earlier than the PEW score 9/26. High sensitivity and negative predictive value for RAPID Index was associated with low specificity and low positive predictive value. We conclude that it is feasible to collect clinically valid physiological data wirelessly for 50% of intended monitoring time. The RAPID Index identified more deterioration, before the PEW score, but has a low specificity. By using the RAPID Index with a PEW system some life-threatening events may be averted

Improved data visualisation through multiple dissimilarity modelling

Popular dimension reduction and visualisation algorithms rely on the assumption that input dissimilarities are typically Euclidean, for instance Metric Multidimensional Scaling, t-distributed Stochastic Neighbour Embedding and the Gaussian Process Latent Variable Model. It is well known that this assumption does not hold for most datasets and often high-dimensional data sits upon a manifold of unknown global geometry. We present a method for improving the manifold charting process, coupled with Elastic MDS, such that we no longer assume that the manifold is Euclidean, or of any particular structure. We draw on the benefits of different dissimilarity measures allowing for the relative responsibilities, under a linear combination, to drive the visualisation process

The behaviour of political parties and MPs in the parliaments of the Weimar Republic

Copyright @ 2012 The Authors. This is the author's accepted manuscript. The final published article is available from the link below.Analysing the roll-call votes of the MPs of the Weimar Republic we find: (1) that party competition in the Weimar parliaments can be structured along two dimensions: an economic left–right and a pro-/anti-democratic. Remarkably, this is stable throughout the entire lifespan of the Republic and not just in the later years and despite the varying content of votes across the lifespan of the Republic, and (2) that nearly all parties were troubled by intra-party divisions, though, in particular, the national socialists and communists became homogeneous in the final years of the Republic.Zukunftskolleg, University of Konstan