The structure of Estonian personal values: a lexical approach

2002 ◽  
Vol 16 (3) ◽  
pp. 221-235 ◽  
Author(s):  
Toivo Aavik ◽  
Jüri Allik
Keyword(s):  
Principal Component Analysis ◽  
Personal Values ◽  
General Structure ◽  
Principal Component ◽  
Higher Order ◽  
The Self ◽  
The Other ◽  
Two Dimensional ◽  
Lexical Approach ◽  
Estonian Language

The main purpose of this paper is to investigate the variety of value describing words and interrelation of value categories in the Estonian language. To accomplish this aim, a psycholexical approach was adopted, during which a set of 560 value‐related words was selected from the Estonian Orthological Dictionary and the results were compared with the Schwartz Values Survey (SVS). When principal‐component analysis was applied on the self‐ratings of a reduced list of 78 value‐related words, six factors emerged and were labelled as benevolence, self‐enhancement, broadmindedness, hedonism, conservatism, and self‐realization. However, all these themes are interrelated and load on a singular secondary dimension. The constructs measured by SVS and the value categories in Estonian were only partially interchangeable; moderate correlations imply an imperfect correspondence: each theme was related to many categories on the other questionnaire. However, a significant general structure refers to the same two‐dimensional level of higher‐order values described by Schwartz in 1992. Copyright © 2002 John Wiley & Sons, Ltd.

scholarly journals Discrimination of brazilian red varietal wines according to their sensory descriptors

2011 ◽  
Vol 35 (6) ◽  
pp. 1172-1176 ◽  
Author(s):  
Alberto Miele ◽  
Luiz Antenor Rizzon
Keyword(s):  
Principal Component Analysis ◽  
Total Variation ◽  
Principal Component ◽  
Pinot Noir ◽  
The Other ◽  
Sensory Characteristics ◽  
Cabernet Sauvignon ◽  
Sensory Panel ◽  
Red Grape ◽  
Grape Varieties

The purpose of this paper was to establish the sensory characteristics of wines made from old and newly introduced red grape varieties. To attain this objective, 16 Brazilian red varietal wines were evaluated by a sensory panel of enologists who assessed wines according to their aroma and flavor descriptors. A 90 mm unstructured scale was used to quantify the intensity of 26 descriptors, which were analyzed by means of the Principal Component Analysis (PCA). The PCA showed that three important components represented 74.11% of the total variation. PC 1 discriminated Tempranillo, Marselan and Ruby Cabernet wines, with Tempranillo being characterized by its equilibrium, quality, harmony, persistence and body, as well as by, fruity, spicy and oaky characters. The other two varietals were defined by vegetal, oaky and salty characteristics; PC 2 discriminated Pinot Noir, Sangiovese, Cabernet Sauvignon and Arinarnoa, where Pinot Noir was characterized by its floral flavor; PC 3 discriminated only Malbec, which had weak, floral and fruity characteristics. The other varietal wines did not show important discriminating effects.

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

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