Search results for "DIMENSION"
showing 10 items of 2766 documents
Dimensionality reduction via regression on hyperspectral infrared sounding data
2014
This paper introduces a new method for dimensionality reduction via regression (DRR). The method generalizes Principal Component Analysis (PCA) in such a way that reduces the variance of the PCA scores. In order to do so, DRR relies on a deflationary process in which a non-linear regression reduces the redundancy between the PC scores. Unlike other nonlinear dimensionality reduction methods, DRR is easy to apply, it has out-of-sample extension, it is invertible, and the learned transformation is volume-preserving. These properties make the method useful for a wide range of applications, especially in very high dimensional data in general, and for hyperspectral image processing in particular…
Scaling Up a Metric Learning Algorithm for Image Recognition and Representation
2008
Maximally Collapsing Metric Learning is a recently proposed algorithm to estimate a metric matrix from labelled data. The purpose of this work is to extend this approach by considering a set of landmark points which can in principle reduce the cost per iteration in one order of magnitude. The proposal is in fact a generalized version of the original algorithm that can be applied to larger amounts of higher dimensional data. Exhaustive experimentation shows that very similar behavior at a lower cost is obtained for a wide range of the number of landmark points used.
The Three Steps of Clustering In The Post-Genomic Era
2013
This chapter descibes the basic algorithmic components that are involved in clustering, with particular attention to classification of microarray data.
A Feature Set Decomposition Method for the Construction of Multi-classifier Systems Trained with High-Dimensional Data
2013
Data mining for the discovery of novel, useful patterns, encounters obstacles when dealing with high-dimensional datasets, which have been documented as the "curse" of dimensionality. A strategy to deal with this issue is the decomposition of the input feature set to build a multi-classifier system. Standalone decomposition methods are rare and generally based on random selection. We propose a decomposition method which uses information theory tools to arrange input features into uncorrelated and relevant subsets. Experimental results show how this approach significantly outperforms three baseline decomposition methods, in terms of classification accuracy.
Regularized Regression Incorporating Network Information: Simultaneous Estimation of Covariate Coefficients and Connection Signs
2014
We develop an algorithm that incorporates network information into regression settings. It simultaneously estimates the covariate coefficients and the signs of the network connections (i.e. whether the connections are of an activating or of a repressing type). For the coefficient estimation steps an additional penalty is set on top of the lasso penalty, similarly to Li and Li (2008). We develop a fast implementation for the new method based on coordinate descent. Furthermore, we show how the new methods can be applied to time-to-event data. The new method yields good results in simulation studies concerning sensitivity and specificity of non-zero covariate coefficients, estimation of networ…
Incrementally Assessing Cluster Tendencies with a~Maximum Variance Cluster Algorithm
2003
A straightforward and efficient way to discover clustering tendencies in data using a recently proposed Maximum Variance Clustering algorithm is proposed. The approach shares the benefits of the plain clustering algorithm with regard to other approaches for clustering. Experiments using both synthetic and real data have been performed in order to evaluate the differences between the proposed methodology and the plain use of the Maximum Variance algorithm. According to the results obtained, the proposal constitutes an efficient and accurate alternative.
Some varieties of algebras of polynomial growth
2008
We determine a complete list of finite dimensional algebras generating the subvarieties of var(G) and var(UT_2).
¿Estrellas o constelaciones? Implicaciones de los estudios cognitivos para el modelo dimensional de la emoción
2016
Uno de los principales debates dentro del estudio de la psicología de la emoción concierne a la concepción de las emociones como constructos psicológicos unificados (categoriales/discretos) en contraposición con el enfoque di-mensional del episodio emocional. En este marco, el modelo dimensional de Russell (2003) destaca en el panorama académico al constituir una propuesta integradora que da cuenta de una serie de problemas históricos del ámbito de estudio. Con este fin, se realiza una aproximación analítica a dicho modelo y se argumenta su viabilidad. El presente artículo tiene como objetivo revisar la evidencia empírica a favor de cuatro hipótesis que sostiene el modelo dimensional de Rus…
From Mood to Meaning: The Changing Model of the User in Entertainment Research
2015
In recent years, entertainment theory has undergone a paradigmatic shift: The traditional conceptualization of entertainment as an exclusively pleasurable affective state has been significantly extended by recent two-factor models. These models have introduced a second dimension of entertainment that incorporates more complex nonhedonic experiences, such as the search for meaning or intrinsic need satisfaction. They have not only crucially altered the way communication scholars conceptualize the audience of media entertainment but also our discipline's view on the effects of entertaining media content. The present article discusses the implications of this changing model of the media user b…
A Functional Near-Infrared Spectroscopy Examination of the Neural Correlates of Cognitive Shifting in Dimensional Change Card Sort Task
2020
This study aims to examine the neural correlates of cognitive shifting during the Dimensional Change Card Sort Task (DCCS) task with functional near-infrared spectroscopy. Altogether 49 children completed the DCCS tasks, and 25 children (Mage = 68.66, SD = 5.3) passing all items were classified into the Switch group. Twenty children (Mage = 62.05, SD = 8.13) committing more than one perseverative errors were grouped into the Perseverate group. The Switch group had Brodmann Area (BA) 9 and 10 activated in the pre-switch period and BA 6, 9, 10, 40, and 44 in the post-switch period. In contrast, the Perseverate group had BA 9 and 10 activated in the pre-switch period and BA 8, 9, 10 in the pos…