1,721,062 research outputs found
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
On Compressive Ensemble Induced Regularisation::How Close is the Finite Ensemble Precision Matrix to the Infinite Ensemble?
No full textAveraging ensembles of randomly oriented low-dimensional projections of a singular co-variance represent a novel and attractive means to obtain a well-conditioned inverse, which only needs access to random projections of the data. However, theoretical analyses so far have only been done at convergence, implying good properties for ‘large-enough’ ensembles. But how large is ‘large enough’? Here we bound the expected difference in spectral norm between the finite ensemble precision matrix and the infinite ensemble, and based on this we give an estimate of the required ensemble size to guarantee the approximationerror of the finite ensemble is below a given tolerance. Under mild assumptions, we find that for any given tolerance, the ensemble only needs to grow linearly in the original data dimension. A technical ingredient of our analysis is to upper bound the spectral norm of a matrix-variate T, which we then employ in conjunction with specific results from random matrix theory regarding the estimation of the covariance of random matrices
Non-asymptotic analysis of compressive Fisher discriminants in terms of the effective dimension
Full text linkWe provide a non-asymptotic analysis of the generalisation error of compressive Fisher linear discriminant (FLD) classification that is dimension free under mild assumptions. Our analysis includes the effects that random projection has on classification performance under covariance model misspecification, as well as various good and bad effects of random projections that contribute to the overall performance of compressive FLD. We also give an asymptotic bound as a corollary of our finite sample result. An important ingredient of our analysis is to develop new dimension-free bounds on the largest and smallest eigenvalue of the compressive covariance, which may be of independent interest
A New Look at Nearest Neighbours: Identifying Benign Input Geometries via Random Projections
Full text linkIt is well known that in general, the nearest neighbour rule (NN) has sample complexity that is exponential in the input space dimension d when only smoothness is assumed on the label posterior function. Here we consider NN on randomly projected data, and we show that, if the input domain has a small ”metric size”, then the sample complexity becomes exponential in the metric entropy integral of the set of normalised chords of the input domain. This metric entropy integral measures the complexity of the input domain, and can be much smaller than d – for instance in cases when the data lies in a linear or a smoothnonlinear subspace of the ambient space, or when it has a sparse representation. We then show that the guarantees we obtain for the compressive NN also hold for the dataspace NN in bounded domains; thus the random projection takes the role of an analytic tool to identify benign structures under which NN learning is possible from a small sample size. Numerical simulations on data designed to have intrinsically low complexity confirm our theoretical findings, and display a striking agreement in the empirical performances of compressive NN and dataspace NN. This suggests that high dimensional data sets that have a lowcomplexity underlying structure are well suited for computationally cheap compressive NN learning.<br/
Dimension-free error bounds from random projections
Full text linkLearning from high dimensional data is challenging in general – however, often the data is not truly high dimensional in the sense that it may have some hidden low complexity geometry. We give new, user-friendly PAC-bounds that are able to take advantage of such benign geometry to reduce dimensional-dependence of error-guarantees in settings where such dependence is known to be essential in general. This is achieved by employing random projection as an analytic tool, and exploiting its structure-preserving compression ability. We introduce an auxiliary function class that operates on reduced dimensional inputs, and a new complexity term, as the distortion of the loss under random projections. The latter is a hypothesis-dependent data-complexity, whose analytic estimates turn out to recover various regularisation schemes in parametric models, and a notion of intrinsic dimension, as quantified by the Gaussian width of the input support in the case of the nearest neighbour rule. If there is benign geometry present, then the bounds become tighter, otherwise they recover the original dimension-dependent bounds
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