1,721,443 research outputs found
Bayesian fuzzy clustering of colored graphs.
No full textWith the increasing availability of interaction data stemming form fields as diverse as systems biology, telecommunication or social sciences, the task of mining and understanding the underlying graph structures becomes more and more important. Here we focus on data with different types of nodes; we subsume this meta information in the color of a node. An important first step is the unsupervised clustering of nodes into communities, which are of the same color and highly connected within but sparsely connected to the rest of the graph. Recently we have proposed a fuzzy extension of this clustering concept, which allows a node to have membership in multiple clusters. The resulting gradient descent algorithm shared many similarities with the multiplicative update rules from nonnegative matrix factorization. Two issues left open were the determination of the number of clusters of each color, as well as the non-defined integration of additional prior information. In this contribution we resolve these issues by reinterpreting the factorization in a Bayesian framework, which allows the ready inclusion of priors. We integrate automatic relevance determination to automatically estimate group sizes. We derive a maximum-a-posteriori estimator, and illustrate the feasibility of the approach on a toy as well as a protein-complex hypergraph, where the resulting fuzzy clusters show significant enrichment of distinct gene ontology categories
Bayesian inference of latent causes in gene regulatory dynamics.
No full textIn the study of gene regulatory networks, more and more quantitative data becomes available. However, few of the players in such networks are observed, others are latent. Focusing on the inference of multiple such latent causes, we arrive at a blind source separation problem. Under the assumptions of independent sources and Gaussian noise, this condenses to a Bayesian independent component analysis problem with a natural dynamic structure. We here present a method for the inference in networks with linear dynamics, with a straightforward extension to the nonlinear case. The proposed method uses a maximum a posteriori estimate of the latent causes, with additional prior information guaranteeing independence. We illustrate the feasibility of our method on a toy example and compare the results with standard approaches
To infinity and beyond: On ICA over Hilbert Spaces
No full textThe original Independent Component Analysis (ICA) problem of blindly separating a mixture of a finite number of real-valued statistically independent one-dimensional sources has been extended in a number of ways in recent years. These include dropping the assumption that all sources are one-dimensional and some extensions to the case where the sources are not real-valued. We introduce an extension in a further direction, no longer assuming only a finite number of sources, but instead allowing infinitely many. We define a notion of independent sources for this case and show separability of ICA in this framework
Joint Diagonalization of Several Scatter Matrices for ICA
No full textProcedures such as FOBI that jointly diagonalize two matrices with the independence property have a long tradition in ICA. These procedures have well-known statistical properties, for example they are prone to failure if the sources have multiple identical values on the diagonal. In this paper we suggest to diagonalize jointly scatter matrices havingthe independence property. For the joint diagonalization we suggest a novel algorithm which finds the correct direction in an deflation-based manner, one after another. The resulting algorithm can also handle degenerate signals and is robust against noise. This is demonstrated in a simulation study
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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