A new incremental principal component analysis with a forgetting factor for background estimation

2014 ◽  
Author(s):  
Takashi Toriu ◽  
Thi Thi Zin ◽  
Hiromitsu Hama
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Forgetting Factor ◽  
Background Estimation

scholarly journals Background Estimation Using Principal Component Analysis Based on Limited Memory Block Krylov Subspace Optimization

2018 ◽  
Vol 8 (5) ◽  
pp. 2847
Author(s):  
Ilmiyati Sari ◽  
Asep Juarna ◽  
Suryadi Harmanto ◽  
Djati Kerami
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Krylov Subspace ◽  
Principal Component ◽  
Component Analysis ◽  
Background Component ◽  
Background Estimation ◽  
Memory Block ◽  
Limited Memory ◽  
Subspace Optimization

<p>Given a video of 𝑀 frames of size ℎ × 𝑤. Background components of a video are the elements matrix which relative constant over 𝑀 frames. In PCA (principal component analysis) method these elements are referred as “principal components”. In video processing, background subtraction means excision of background component from the video. PCA method is used to get the background component. This method transforms 3 dimensions video (ℎ × 𝑤 × 𝑀) into 2 dimensions one (𝑁 × 𝑀), where 𝑁 is a linear array of size ℎ × 𝑤. The principal components are the dominant eigenvectors which are the basis of an eigenspace. The limited memory block Krylov subspace optimization then is proposed to improve performance the computation. Background estimation is obtained as the projection each input image (the first frame at each sequence image) onto space expanded principal component. The procedure was run for the standard dataset namely SBI (Scene Background Initialization) dataset consisting of 8 videos with interval resolution [146 150, 352 240], total frame [258,500]. The performances are shown with 8 metrics, especially (in average for 8 videos) percentage of error pixels (0.24%), the percentage of clustered error pixels (0.21%), multiscale structural similarity index (0.88 form maximum 1), and running time (61.68 seconds). </p>

scholarly journals Statistical Analysis of Nonlinear Processes Based on Penalty Factor

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yingwei Zhang ◽  
Chuanfang Zhang ◽  
Wei Zhang
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Feature Space ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Forgetting Factor ◽  
Analysis Algorithm ◽  
Penalty Factor ◽  
New Process ◽  
Principal Component Analysis Algorithm

A new process monitoring approach is proposed for handling the nonlinear monitoring problem in the electrofused magnesia furnace (EFMF). Compared to conventional method, the contributions are as follows: (1) a new kernel principal component analysis is proposed based on loss function in the feature space; (2) the model of kernel principal component analysis based on forgetting factor is updated; (3) a new iterative kernel principal component analysis algorithm is proposed based on penalty factor.

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.

Sign in / Sign up

Export Citation Format

Share Document