Research on the Influence of Fabric Properties on Men’s Suits Shape

2011 ◽  
Vol 175-176 ◽  
pp. 993-998
Author(s):  
Xiu Qin Hu ◽  
Yan Chen
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
High Quality ◽  
Systematic Experiment ◽  
Components Analysis ◽  
Design And Manufacture ◽  
Selection Of

The shape is an important factor to form the garment style and manifest its beauty. It is affected not only by the style and design but also by the fabric properties. A systematic experiment was carried out by selecting twenty commonly used suit fabrics and making use of KES fabrics instrument. The main indexes were drawn by Principal Components Analysis (PCA), which mainly influenced the men’s suits shape. The obtained results will surely help to make the selection of fabric easier, provide the reference to the men’s suit design and manufacture, and thus produce high-quality men’s suits.

scholarly journals A method of dimensionality reduction by selection of components in principal component analysis for text classification

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1499-1506 ◽  
Author(s):  
Yangwu Zhang ◽  
Guohe Li ◽  
Heng Zong
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Principal Components Analysis ◽  
Text Classification ◽  
Principal Components ◽  
Principal Component ◽  
Method Of Dimensionality Reduction ◽  
Components Analysis ◽  
Selection Of

Dimensionality reduction, including feature extraction and selection, is one of the key points for text classification. In this paper, we propose a mixed method of dimensionality reduction constructed by principal components analysis and the selection of components. Principal components analysis is a method of feature extraction. Not all of the components in principal component analysis contribute to classification, because PCA objective is not a form of discriminant analysis (see, e.g. Jolliffe, 2002). In this context, we present a function of components selection, which returns the useful components for classification by the indicators of the performances on the different subsets of the components. Compared to traditional methods of feature selection, SVM classifiers trained on selected components show improved classification performance and a reduction in computational overhead.

The Golf Players' Motivations: The Algarve Case

2006 ◽  
Vol 6 (3) ◽  
pp. 227-238 ◽  
Author(s):  
Antonia Correia ◽  
Pedro Pintassilgo
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Social Environment ◽  
Principal Components ◽  
Golf Courses ◽  
The Sun ◽  
Market Segments ◽  
The Third ◽  
Components Analysis ◽  
Selection Of

The purpose of this article is to investigate the motivations behind golf demand in the Algarve — one of Europe's most popular golf destinations. The research is based on the results of a survey on the golf demand of Algarve's golf courses, held in 2002. In order to identify the main motives behind golf demand in the region, a principal components analysis was performed. Four main choice factors were identified to explain the selection of Algarve's golf courses. The first was designated social environment and is associated with motives such as events and beaches. The second, leisure, is related to restaurants and bars, landscape, weather and accommodation. The third, entitled golf, is directly related to characteristics of courses. The fourth, logistics, is associated with variables such as price and accessibility. It is also found, through a cluster analysis that the choice factors can be associated with three market segments: the tourist golfer, who is mostly concerned with the golf courses and the game; the householder golfer, essentially centred on accommodation, gastronomy, landscape, weather, price and accessibility; and finally, the sun-beach tourist, who is mostly interested in tourist opportunities.

scholarly journals Identification of Tibicen cicada species by a Principal Components Analysis of their songs

2004 ◽  
Vol 76 (2) ◽  
pp. 442-444 ◽  
Author(s):  
Eiji Ohya
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Pulse Rate ◽  
Principal Components ◽  
High Quality ◽  
Specific Identification ◽  
Components Analysis

Specific identification of three Tibicen cicadas, T. japonicus, T. flammatus and T. bihamatus, by their chirping sounds was carried out using Principal Components Analysis (PCA). High quality recordings of each species were used as the standards. The peak and mean frequencies and the pulse rate were used as the variables. Out of 12 samples recorded in the fields one fell in the vicinity of T. japonicus and all other were positioned near T. bihamatus. Then the cluster analysis of the PCA scores clearly separated each species and allocated the samples in the same way.

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