1,720,977 research outputs found
Inexact higher-order proximal algorithms for tensor factorization
No full textIn the last decades, Matrix Factorization (MF) models and their multilinear extension-Tensor Factorization (TF) models have been shown to be powerful tools for high dimensional data analysis and features extraction. Computing MF's or TF's are commonly achieved by solving a constrained optimization subproblem on each block of variables, where the subproblems usually have a huge problem size that one has to rely on First-order Methods (FoM), i.e., gradient-based optimization methods. In this work, we consider Higher-order Methods (HoM), which are based on higher-order derivatives of the objective function. Compared to FoM, HoM are faster both in theory and practice. However, HoM has a higher per-iteration cost than FoM. Based on the recent development of efficient and implementable HoM, we consider higher-order proximal point methods within the BLUM framework which is potentially tractable for large-scale problems. For the newly proposed HoM, we introduce the appropriate objective functions, derive the algorithm, and show experimentally that the drop in the number of iterations with respect to their per-iteration cost make these HoM-based algorithms attractive for computing MF's and TF's
Blind audio source separation with minimum-volume beta-divergence NMF
No full textConsidering a mixed signal composed of various audio sources and recorded with a single microphone, we consider in this paper the blind audio source separation problem which consists in isolating and extracting each of the sources. To perform this task, nonnegative matrix factorization (NMF) based on the Kullback-Leibler and Itakura-Saito β-divergences is a standard and state-of-the-art technique that uses the time-frequency representation of the signal. We present a new NMF model better suited for this task. It is based on the minimization of β-divergences along with a penalty term that promotes the columns of the dictionary matrix to have a small volume. Under some mild assumptions and in noiseless conditions, we prove that this model is provably able to identify the sources. In order to solve this problem, we propose multiplicative updates whose derivations are based on the standard majorization-minimization framework. We show on several numerical experiments that our new model is able to obtain more interpretable results than standard NMF models. Moreover, we show that it is able to recover the sources even when the number of sources present into the mixed signal is overestimated. In fact, our model automatically sets sources to zero in this situation, hence performs model order selection automatically
Séparation aveugle de sources sonores par factorization en matrices positives avec pénalité sur le volume du dictionnaire
No full textAudio source separation concerns techniques used to extract unknown signals called sources from a mixed signal. In this paper, we assume that the audio signal is recorded with a single microphone. Considering a mixed signal composed of various audio sources, the blind audio source separation consists in isolating and extracting each of the sources on the basis of a single recording. Usually, the only known information is the number of estimated sources present in the mixed signal. Based on a time-frequency representation of the signal, classical source separation techniques integrate algorithms such as nonnegative matrix factorization (NMF). Optimization problems in blind audio source separation are based on the minimization of criteria such as the Kullback-Leibler and Itakura-Saito divergences, both divergences belonging to the family of β-divergences. In this paper, we present a new model of separation based on the minimization of the Kullback-Leibler includinga penalty term promoting the columns of the dictionary matrix to have small volume. In order to solve this problem, the global cost function is replaced by a convex and separable auxiliary function that will be minimized. We will show that we obtain more interpretable results in the case where the factorization rank (that is, the number of sources present into the mixed signal) is overestimated
Minimum-volume Rank-deficient Nonnegative Matrix Factorizations
No full textIn recent years, nonnegative matrix factorization (NMF) with volume regularization has been shown to be a powerful identifiable model; for example for hyperspectral unmixing, document classification, community detection and hidden Markov models. In this paper, we show that minimum-volume NMF (min-vol NMF) can also be used when the basis matrix is rank deficient, which is a reasonable scenario for some real-world NMF problems (e.g., for unmixing multispectral images). We propose an alternating fast projected gradient method for min-vol NMF and illustrate its use on rank-deficient NMF problems; namely a synthetic data set and a multispectral image
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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