1,720,965 research outputs found
Detection of change by L1-norm principal-component analysis
No full textWe consider the problem of detecting a change in an arbitrary vector process by examining the evolution of calculated data subspaces. In our developments, both the data subspaces and the change identification criterion are novel and founded in the theory of L1-norm principal-component analysis (PCA). The outcome is highly accurate, rapid detection of change in streaming data that vastly outperforms conventional eigenvector subspace methods (L2-norm PCA). In this paper, illustrations are offered in the context of artificial data and real electroencephalography (EEG) and electromyography (EMG) data sequences
FFT calculation of the L1-norm principal component of a data matrix
No full textThis paper presents a fast approximate rank-1 L1-norm Principal Component Analysis (L1-PCA) estimator implemented in the Fourier domain. Specifically, we first rephrase the problem of rank-1 L1-PCA estimation as a cyclic shift parameter estimation and then we present an algorithm for estimating the first L1-norm Principal Component (L1-PC) in the Fourier domain, practically using FFT. The proposed method is shown to be asymptotically efficient and our numerical studies corroborate its performance merits
Cloud-assisted individual l1-PCA face recognition using wavelet-domain compressed images
No full textFace recognition has been an active research field for a long time, and recently new challenges have arisen in designing cloud-assisted face recognition algorithms. In a cloud assisted face recognition system, mobile devices acquire the data images; then, in order to unbind the cloud face recognition algorithm from the particular features extracted at the mobile device, the images are encoded and uploladed into the cloud. In this framework, it is important to understand and control the effect of the image compression stage performed at the mobile device on the performances of the face recognition algorithms realized within the cloud. Here, we analyze the impact of wavelet domain image compression on the Individual Adaptive (IA) L1-PCA subspace computation and assess the performance of a classifier operating on data characterized by increasing compactness and accordingly decreasing accuracy
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
Deep L1-PCA of time-variant data with application to brain connectivity measurements
No full textL1-Principal Component Analysis (L1-PCA) is a powerful computational tool to identify relevant components in data affected by noise, outliers, partial disruption and so on. Relevant efforts have been made to adapt its powerful summarization capacity to time variant data, e.g. in tracking the evolution of L1-PCA components. Here, we analyze a layered version of L1-PCA, to which we refer to as Deep L1-PCA. Deep L1-PCA is obtained by recursive application of two stages: estimation of L1-PCA basis and extraction of the first rank projector. The Deep L1-PCA is applied to repeated EEG connectivity measures and it proves relevant for identifying outliers, changes, and stable components. Moreover, at each layer, an in-depth analysis of the mean square error between the data applied at the input layer and the output projector is provided. The Deep L1-PCA allows to cope with outliers of different temporal extent as well as to extract the relevant common component at a reduced computational cost
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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