1,720,987 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Get PDFWe 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
Corrected proof of the result of ‘A prediction error property of the Lasso estimator and its generalization’ by Huang (2003)
Get PDFThe Lasso achieves variance reduction and variable selection by solving an ℓ 1 -regularized least squares problem. Huang (2003) claims that ‘there always exists an interval of regularization parameter values such that the corresponding mean squared prediction error for the Lasso estimator is smaller than for the ordinary least square estimator’. This result is correct. However, its proof in Huang (2003) is not. This paper presents a corrected proof of the claim, which exposes and uses some interesting fundamental properties of the Lasso.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72873/1/j.1467-842X.2004.00347.x.pd
On the cross-validation bias due to unsupervised pre-processing
Get PDFCross-validation is the de facto standard for predictive model evaluation and
selection. In proper use, it provides an unbiased estimate of a model's
predictive performance. However, data sets often undergo various forms of
data-dependent preprocessing, such as mean-centering, rescaling, dimensionality
reduction, and outlier removal. It is often believed that such preprocessing
stages, if done in an unsupervised manner (that does not incorporate the class
labels or response values) are generally safe to do prior to cross-validation.
In this paper, we study three commonly-practiced preprocessing procedures
prior to a regression analysis: (i) variance-based feature selection; (ii)
grouping of rare categorical features; and (iii) feature rescaling. We
demonstrate that unsupervised preprocessing can, in fact, introduce a
substantial bias into cross-validation estimates and potentially hurt model
selection. This bias may be either positive or negative and its exact magnitude
depends on all the parameters of the problem in an intricate manner. Further
research is needed to understand the real-world impact of this bias across
different application domains, particularly when dealing with small sample
sizes and high-dimensional data.Comment: 31 pages, 6 figures, 1 table. New sections: (4.2.) Experiments on a
real dataset; (6.) Potential impact on model selection; (7.1.) Upper bounds
based on stability arguments. Updated Fig. 1. with larger sample size
Integrating Random Effects in Deep Neural Networks
Get PDFModern approaches to supervised learning like deep neural networks (DNNs)
typically implicitly assume that observed responses are statistically
independent. In contrast, correlated data are prevalent in real-life
large-scale applications, with typical sources of correlation including
spatial, temporal and clustering structures. These correlations are either
ignored by DNNs, or ad-hoc solutions are developed for specific use cases. We
propose to use the mixed models framework to handle correlated data in DNNs. By
treating the effects underlying the correlation structure as random effects,
mixed models are able to avoid overfitted parameter estimates and ultimately
yield better predictive performance. The key to combining mixed models and DNNs
is using the Gaussian negative log-likelihood (NLL) as a natural loss function
that is minimized with DNN machinery including stochastic gradient descent
(SGD). Since NLL does not decompose like standard DNN loss functions, the use
of SGD with NLL presents some theoretical and implementation challenges, which
we address. Our approach which we call LMMNN is demonstrated to improve
performance over natural competitors in various correlation scenarios on
diverse simulated and real datasets. Our focus is on a regression setting and
tabular datasets, but we also show some results for classification. Our code is
available at https://github.com/gsimchoni/lmmnn.Comment: 53 pages, 9 figure
Mixed Semi-Supervised Generalized-Linear-Regression with applications to Deep-Learning and Interpolators
Get PDFWe present a methodology for using unlabeled data to design semi supervised
learning (SSL) methods that improve the prediction performance of supervised
learning for regression tasks. The main idea is to design different mechanisms
for integrating the unlabeled data, and include in each of them a mixing
parameter , controlling the weight given to the unlabeled data.
Focusing on Generalized Linear Models (GLM) and linear interpolators classes of
models, we analyze the characteristics of different mixing mechanisms, and
prove that in all cases, it is invariably beneficial to integrate the unlabeled
data with some nonzero mixing ratio , in terms of predictive
performance. Moreover, we provide a rigorous framework to estimate the best
mixing ratio where mixed SSL delivers the best predictive
performance, while using the labeled and unlabeled data on hand.
The effectiveness of our methodology in delivering substantial improvement
compared to the standard supervised models, in a variety of settings, is
demonstrated empirically through extensive simulation, in a manner that
supports the theoretical analysis. We also demonstrate the applicability of our
methodology (with some intuitive modifications) to improve more complex models,
such as deep neural networks, in real-world regression tasks.Comment: 63 pages, 10 figure
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