scholarly journals Factorial Analysis of Mixed Data

2014 ◽  
pp. 67-78 ◽  
Author(s):  
Jérôme Pagès
Keyword(s):  
Factorial Analysis ◽  
Mixed Data

Appropriation des Savoirs et Mémoire Collective

2013 ◽  
Vol 72 (2) ◽  
pp. 71-78
Author(s):  
Sophie Richardot
Keyword(s):  
Collective Memory ◽  
Factorial Analysis ◽  
Experimental Results ◽  
Ancient History ◽  
Historical Events ◽  
Memoire Collective ◽  
Obedience To Authority

The aim of this study is to understand to what extent soliciting collective memory facilitates the appropriation of knowledge. After being informed about Milgram’s experiment on obedience to authority, students were asked to mention historical or contemporary events that came to mind while thinking about submission to authority. Main results of the factorial analysis show that the students who do not believe in the reproducibility of the experimental results oppose dramatic past events to a peaceful present, whereas those who do believe in the reproducibility of the results also mention dramatic contemporary events, thus linking past and present. Moreover, the students who do not accept the results for today personify historical events, whereas those who fully accept them generalize their impact. Therefore, according to their attitude toward this objet of knowledge, the students refer to two kinds of memory: a “closed memory,” which tends to relegate Milgram’s results to ancient history; and an “open memory,” which, on the contrary, transforms past events into a concept that helps them understand the present. Soliciting collective memory may contribute to the appropriation of knowledge provided the memory activated is an “open” one, linking past to present and going beyond the singularity of the event.

Location Model for Mixed Data Using Winsorization with Comedian

2018 ◽  
Vol 9 (12) ◽  
pp. 1847-1850
Author(s):  
LathaV LathaV ◽  
P Rajalakshmi
Keyword(s):  
Location Model ◽  
Mixed Data

Measuring the Information Content of the Beige Book: A Mixed Data Sampling Approach

2008 ◽  
Author(s):  
Michelle T. Armesto ◽  
Ruben Hernandez-Murillo ◽  
Michael Owyang ◽  
Jeremy M. Piger
Keyword(s):  
Information Content ◽  
Mixed Data ◽  
Data Sampling ◽  
Mixed Data Sampling ◽  
Sampling Approach

Information and scientific impact of Advanced Therapies in the age of mass media: an Altmetrics-based analysis of Tissue Engineering. (Preprint)

2020 ◽  
Author(s):  
Antonio Santisteban ◽  
Julia Moran ◽  
Miguel Ángel Martín Piedra ◽  
Antonio Campos Muñoz ◽  
José Antonio Moral Muñoz ◽  
...  
Keyword(s):  
Tissue Engineering ◽  
Mass Media ◽  
Prediction Equation ◽  
Factorial Analysis ◽  
Online Media ◽  
Research Groups ◽  
Academic Institutions ◽  
Scientific Impact ◽  
Online Platforms ◽  
The Impact

BACKGROUND Tissue engineering (TE) constitutes a multidisciplinary field aiming to construct artificial tissues to regenerate end-staged organs. Its development has taken placed since the last decade of the 20th century, entailing a clinical revolution. In this sense, TE research groups have worked and shared relevant information in the mass media era. Thus, it would be interesting to study the online dimension of TE research and to compare it with traditional measures of scientific impact. OBJECTIVE To evaluate TE online dimension from 2012 to 2018 by using metadata obtained from the Web of Science (WoS) and Altmetrics and to develop a prediction equation for the impact of TE documents from Almetrics scores. METHODS We have analyzed 23,719 TE documents through descriptive and statistical methods. First, TE temporal evolution was exposed for WoS and fifteen online platforms (News, Blogs, Policy, Twitter, Patents, Peer review, Weibo, Facebook, Wikipedia, Google, Reddit, F1000, Q&A, Video and Mendeley readers). The 10 most-cited TE original articles were ranked according to WoS citations and the Altmetric Attention Score. Second, in order to better comprehend TE online framework, a correlation and factorial analysis were performed based on the suitable results previously obtained for the Bartlett Sphericity and Kaiser-Meyer-Olkin tests. Finally, the liner regression model was applied to elucidate the relation between academy and online media and to construct a prediction equation for TE from Altmetrics data. RESULTS TE dynamic shows an upward trend in WoS citations, Twitter, Mendeley Readers and Altmetric Scores. However, WoS and Altmetrics rankings for the most cited documents clearly differs. When compared, the best correlation results were obtained for Mendeley readers and WoS (ρ=0.71). In addition, the factorial analysis identified six factors that could explain the previously observed differences between TE academy, and the online platforms evaluated. At this point, the mathematical model constructed is able to predict and explain more than the 40% of TE WoS citations from Altmetrics scores. CONCLUSIONS The scientific information related to the construction of bioartificial tissues increasingly reaches society through different online media. Because of the focus of TE research importantly differs when the academic institutions and online platforms are compared, it could be stated that basic and clinical research groups, academic institutions and health politicians should take it into account in a coordinated effort oriented to the design and implementation of adequate strategies for information diffusion and population health education.

scholarly journals Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

2021 ◽  
Vol 13 (2) ◽  
pp. 268
Author(s):  
Xiaochen Lv ◽  
Wenhong Wang ◽  
Hongfu Liu
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Imbalanced Data ◽  
Reconstruction Error ◽  
Hyperspectral Data ◽  
Weight Matrix ◽  
Hyperspectral Images ◽  
Mixed Data ◽  
Sparsity Constraints ◽  
Additional Constraints

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.

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