Análise da estrutura fatorial da versão em português da Escala de Auto-Silenciamento

This study focuses on the adaptation of the Portuguese version of the Escala de Autosilenciamiento [EAS, for its Portuguese acronym] (Neves, 2005) that has been designed to assess the use of cognitive schemas for self silencing in intimate relationships. Participants were 371 women with a mean age of 22.36 years (SD=2.69; Min=18; Max=31), who at that time were involved in affective relationships with an average duration of 39.65 months (SD=33.93; Min=10; Max=192).The exploratory factor analysis suggests a factor solution of three factors, where the first factor includes items from the silencing of the sef and divided self subscales. The second factor includes items from the care-giving subscale such as self-sacrifice. And the third factor includes items from the externalized self-perception subscale. Results of the confirmatory factor analysis show reliable global indices of fitness of the model, confirming the quality of the model in terms of adjustment to empirical data (X2/df=1.964, CFI=.862, GFI=.894, RMSEA=.051) compared to the original model. Implications for a further study of the construct validity of the scale are discussed.Este estudo se centra na adaptação da versão em português da Escala de Auto-silenciamento (EAS; Neves, 2005) que está desenhada para avaliar o uso de esquemas cognitivos de auto-silenciamento nas relações íntimas. Neste estudo participaram 371 mulheres, com uma idade média de 22,36 anos (DP = 2,69, mín. = 18, máx. = 31), que nessa época estavam envolvidas em relações afetivas com uma duração média de 39,65 meses. (DP = 33,93, mín. = 0,10; máx. = 192). Uma análise fatorial exploratória posterior sugere uma solução de três fatores, aonde o primeiro fator inclui os itens da sub-escala de silenciamento do self e do self dividido; o segundo inclui os itens da sub-escala de Provisão de cuidados como o auto sacrifício, e o terceiro inclui os itens da sub-escala de auto percepção externalizada. Os resultados da análise fatorial confirmatória mostram índices gerais de adequação confiáveis, o que confirma a qualidade do ajuste do modelo aos dados empíricos (X2/df = 1964, CFI = .862, GFI = 0,894, RMSEA = 0,051) em comparação com a teste original. Analisam-se as implicações para um estudo más profundo da validez de constructo da escala.Este estudio se centra en la adaptación de la versión en portugués de la Escala de Autosilenciamiento (EAS; Neves, 2005) que está diseñada para evaluar el uso de esquemas cognitivos de autosilenciamiento en las relaciones íntimas. En este estudio participaron 371 mujeres, con una edad media de 22,36 años (DP = 2,69, mín. = 18, máx. = 31), que en esa época estaban involucradas en relaciones afectivas con una duración promedio de 39,65 meses. (DP = 33,93, mín. = 0,10; máx. = 192). Un análisis factorial exploratorio posterior sugiere una solución de tres factores, donde el primer factor incluye los ítems de la subescala de silenciamiento del self y del self dividido; el segundo incluye los ítems de la subescala de Provisión de cuidados como el autosacrificio, y el tercero incluye los ítems de la subescala de autopercepción externalizada. Los resultados del análisis factorial confirmatorio muestran índices generales de adecuación fiables, lo que confirma la calidad del ajuste del modelo a los datos empíricos (X2/df = 1964, CFI = .862, GFI = 0,894, RMSEA = 0,051) en comparación con la prueba original. Se analizan las implicaciones para un estudio más profundo de la validez de constructo de la escala

Two key properties of dimensionality reduction methods

Dimensionality reduction aims at providing faithful low-dimensional representations of high-dimensional data. Its general principle is to attempt to reproduce in a low-dimensional space the salient characteristics of data, such as proximities. A large variety of methods exist in the literature, ranging from principal component analysis to deep neural networks with a bottleneck layer. In this cornucopia, it is rather difficult to find out why a few methods clearly outperform others. This paper identifies two important properties that enable some recent methods like stochastic neighborhood embedding and its variants to produce improved visualizations of high-dimensional data. The first property is a low sensitivity to the phenomenon of distance concentration. The second one is plasticity, that is, the capability to forget about some data characteristics to better reproduce the other ones. In a manifold learning perspective, breaking some proximities typically allow for a better unfolding of data. Theoretical developments as well as experiments support our claim that both properties have a strong impact. In particular, we show that equipping classical methods with the missing properties significantly improves their results

Generalized kernel framework for unsupervised spectral methods of dimensionality reduction

This work introduces a generalized kernel perspective for spectral dimensionality reduction approaches. Firstly, an elegant matrix view of kernel principal component analysis (peA) is described. We show the relationship between kernel peA, and conventional peA using a parametric distance. Secondly, we introduce a weighted kernel peA framework followed from least squares support vector machines (LS-SVM). This approach starts with a latent variable that allows to write a relaxed LS-SVM problem. Such a problem is addressed by a primal-dual formulation. As a result, we provide kernel alternatives to spectral methods for dimensionality reduction such as multidimensional scaling, locally linear embedding, and laplacian eigenmaps; as well as a versatile framework to explain weighted peA approaches. Experimentally, we prove that the incorporation of a SVM model improves the performance of kernel peA

Valid Interpretation of Feature Relevance for Linear Data Mappings

Abstract—Linear data transformations constitute essential operations in various machine learning algorithms, ranging from linear regression up to adaptive metric transformation. Often, linear scalings are not only used to improve the model accuracy, rather feature coefficients as provided by the mapping are interpreted as an indicator for the relevance of the feature for the task at hand. This principle, however, can be misleading in particular for high-dimensional or correlated features, since it easily marks irrelevant features as relevant or vice versa. In this contribution, we propose a mathematical formalisation of the minimum and maximum feature relevance for a given linear transformation which can efficiently be solved by means of linear programming. We evaluate the method in several benchmarks, where it becomes apparent that the minimum and maximum relevance closely resembles what is often referred to as weak and strong relevance of the features; hence unlike the mere scaling provided by the linear mapping, it ensures valid interpretability