Clustering DNA words through distance distributions

Functional data appear in several domains of science, for example, in biomedical, meteorologic or engineering studies. A functional observation can exhibit an atypical behaviour during a short or a large part of the domain and this may be due to magnitude or to shape features. Over the last ten years many outlier detection methods have been proposed. In this work we use the functional data framework to investigate the existence of DNA words with outlying distance distribution, which may be related with biological motifs. A DNA word is a sequence defined in the genome alphabet {ACGT}. Distances between successive occurrences of the same word allow defining the inter-word distance distribution, interpretable as a discrete function. Each word length is associated with a functional dataset formed by 4 distance distributions. As the word length increases, greater is the diversity of observed patterns in the functional dataset and larger is the number of distributions displaying strong peaks of frequency. We propose a two-step procedure to detect words with an outlying pattern of distances: first, the functions are clustered according to their global trend; then, an outlier detection method is applied within each cluster. Each distribution trend is obtained by data smoothing, which avoids some distributions’ peaks, and similarities between smoothed data are explored through hierarchical complete linkage clustering. The dissimilarity between functions is evaluated using the Euclidean distance or the Generalized Minimum distance [1], which considers the dependence between domain points. The resulting dendograms are then cut leading to a partition of the distance distributions. For the second step we use the Directional Outlyingness measure which assigns a robust measure of outlyingness to each domain point and is the building block of a graphical tool for visualization of the centrality of the curves [2]. We focus on the human genome and words of length ≤ 7. Results are compared with those obtained by applying only the second step of the procedure [3].publishe

On preprocessing of speech signals

Preprocessing of speech signals is considered a crucial step in the development of a robust and efficient speech or speaker recognition system. In this paper, we present some popular statistical outlier-detection based strategies to segregate the silence/unvoiced part of the speech signal from the voiced portion. The proposed methods are based on the utilization of the 3 σ edit rule, and the Hampel Identifier which are compared with the conventional techniques: (i) short-time energy (STE) based methods, and (ii) distribution based methods. The results obtained after applying the proposed strategies on some test voice signals are encouragin

Trust-Based Fusion of Untrustworthy Information in Crowdsourcing Applications

In this paper, we address the problem of fusing untrustworthy reports provided from a crowd of observers, while simultaneously learning the trustworthiness of individuals. To achieve this, we construct a likelihood model of the userss trustworthiness by scaling the uncertainty of its multiple estimates with trustworthiness parameters. We incorporate our trust model into a fusion method that merges estimates based on the trust parameters and we provide an inference algorithm that jointly computes the fused output and the individual trustworthiness of the users based on the maximum likelihood framework. We apply our algorithm to cell tower localisation using real-world data from the OpenSignal project and we show that it outperforms the state-of-the-art methods in both accuracy, by up to 21%, and consistency, by up to 50% of its predictions. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved

Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page