scholarly journals RPCA: A Novel Preprocessing Method for PCA

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Samaneh Yazdani ◽  
Jamshid Shanbehzadeh ◽  
Mohammad Taghi Manzuri Shalmani
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Weighting ◽  
Second Step ◽  
Class Structure ◽  
Classification Problems ◽  
Weighting Method ◽  
Preprocessing Method

We propose a preprocessing method to improve the performance of Principal Component Analysis (PCA) for classification problems composed of two steps; in the first step, the weight of each feature is calculated by using a feature weighting method. Then the features with weights larger than a predefined threshold are selected. The selected relevant features are then subject to the second step. In the second step, variances of features are changed until the variances of the features are corresponded to their importance. By taking the advantage of step 2 to reveal the class structure, we expect that the performance of PCA increases in classification problems. Results confirm the effectiveness of our proposed methods.

Sex Assignment to Whole Human Responses in Rorschach

2017 ◽  
Vol 38 (2) ◽  
pp. 108-128
Author(s):  
Christian Mormont ◽  
Patrick Fontan
Keyword(s):  
Principal Component Analysis ◽  
Hierarchical Clustering ◽  
Empirical Investigation ◽  
Principal Component ◽  
Component Analysis ◽  
Second Step ◽  
Chi Square ◽  
Sex Assignment ◽  
Formal Quality

Abstract. According to the theory of identification, men are more likely to qualify their Rorschach human content responses as males, and women as females. These assumptions were tested in an empirical investigation using a Belgian nonpatient sample of 800. All human responses and their location were listed. Analyses were carried out on the 10 Cards and on the formal quality (FQo vs. FQu/−) of all human responses according to the subject’s and the examiner’s sex. Variables were first submitted to principal component analysis, and resulting components were compared in a 2 × 2 design in order to assess examiners’ and participants’ sex potential effects on human responses sex assignments. Univariate and multivariate ANOVA revealed no or only negligible differences. In a second step, distributions of masculine, feminine, and neutral human responses across 16 card locations that commonly elicit human responses were submitted to hierarchical clustering in order to identify masculine, feminine, and neutral locations in Rorschach cards. Chi-square tests revealed no significant association between participants’ sex and human responses locations. Results do not corroborate predictions according to the theory of identification but they do, however, highlight the role of the distal features of blots.

scholarly journals Classification of reduction invariants with improved backpropagation

2002 ◽  
Vol 30 (4) ◽  
pp. 239-247
Author(s):  
S. M. Shamsuddin ◽  
M. Darus ◽  
M. N. Sulaiman
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Feature Space ◽  
Original Data ◽  
Component Analysis ◽  
Classification Problems ◽  
Share Prices ◽  
Data Space ◽  
Backpropagation Network

Data reduction is a process of feature extraction that transforms the data space into a feature space of much lower dimension compared to the original data space, yet it retains most of the intrinsic information content of the data. This can be done by using a number of methods, such as principal component analysis (PCA), factor analysis, and feature clustering. Principal components are extracted from a collection of multivariate cases as a way of accounting for as much of the variation in that collection as possible by means of as few variables as possible. On the other hand, backpropagation network has been used extensively in classification problems such as XOR problems, share prices prediction, and pattern recognition. This paper proposes an improved error signal of backpropagation network for classification of the reduction invariants using principal component analysis, for extracting the bulk of the useful information present in moment invariants of handwritten digits, leaving the redundant information behind. Higher order centralised scale- invariants are used to extract features of handwritten digits before PCA, and the reduction invariants are sent to the improved backpropagation model for classification purposes.

scholarly journals Fault Diagnosis Method Based on Gap Metric Data Preprocessing and Principal Component Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zihan Wang ◽  
Chenglin Wen ◽  
Xiaoming Xu ◽  
Siyu Ji
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Dimensional Space ◽  
Principal Component ◽  
Data Preprocessing ◽  
Component Analysis ◽  
Gap Metric ◽  
Preprocessing Method ◽  
Original Dataset ◽  
Diagnosis Method

Principal component analysis (PCA) is widely used in fault diagnosis. Because the traditional data preprocessing method ignores the correlation between different variables in the system, the feature extraction is not accurate. In order to solve it, this paper proposes a kind of data preprocessing method based on the Gap metric to improve the performance of PCA in fault diagnosis. For different types of faults, the original dataset transformation through Gap metric can reflect the correlation of different variables of the system in high-dimensional space, so as to model more accurately. Finally, the feasibility and effectiveness of the proposed method are verified through simulation.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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