Pca
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PCA (Principal Component Analysis) is a technique used to simplify complex data sets by reducing their dimensionality. It transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. The document provides background on concepts like variance, covariance, and eigenvalues that are important to understanding PCA. It also includes an example of using PCA to analyze student data and identify the most important parameters to describe students.
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Face recognition is used in wide range of application.
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b) Depth and Roll Recorder Assembly
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d) Pressure equalizer
e) Drain/Discharge Valve assembly
f) Bollard Assembly
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SrNo Items Specifications
1 Aluminum Alloy (AlMg5)
Casing Body Material: AlMg5
• Larger Outer Diameter of the Casing: 532.4 MM
• Smaller Outer Diameter of the Casing: 503.05 MM
• Total Length: 1204.20 MM
• Thickness: 6-8 mm
• Structural Details of Casing: The casing is of uniform outer dia for a certain distance from rear side and tapered from a definite distance to the front side. (Refer T-DAP-A1828-GADWG-PH- REV 00)
• Slope of the Tapered Portion: 1/8
• Mass of Casing (Without components mounting, but including the ribs and collars on the body): 58.5 kg
• Maximum External Test Pressure: 12 kgf/cm2
• Maximum Internal Test Pressure:-
i. For Ballast Compartment: 2 kgf/cm2
ii. For Instrument Compartment: 1 kgf/cm2
• Innerspace of casing assembly have 2 compartments:-
i. Ballast Compartment and
ii. Instrument Compartment
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a) Signal Flare Ejector Assembly
b) Depth and Roll Recorder Assembly
c) Light Device
d) Pressure Equalizer
e) Drain/ discharge valve assembly
2 Front Side Collar Material: AlMg5
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• Pitch Circle Diameter: 468 MM
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Pca
- 1. PCA : Principal Component Analysis Author : Nalini Yadav Under Guidance of Prof. K. Rajeshwari
- 2. PCA ï‚› A backbone of modern data analysis. ï‚› A black box that is widely used but poorly understood. ï‚› It is a mathematical tool from applied linear algebra. ï‚› It is a simple, non-parametric method of extracting relevant information from confusing data sets. ï‚› It provides a roadmap for how to reduce a complex data set to a lower dimension
- 3. Background knowledge Va r i a n c e Covariance
- 4. Background knowledge Cova r i a n c e Matrix Eigenvalue
- 5. Background knowledge  Finding the roots of | A – λ .I| will give the eigenvalues and for each of these eigenvalues there will be an eigenvector
- 7. Background knowledge ï‚› Therefore the first eigenvector is any column vector in which the two elements have equal magnitude and opposite sign.
- 9. PCA : Example We collected m parameters about 100 students Height Weight Hair color Average grade … We want to find the most important parameters that best describe a student.
- 10. PCA : Example
- 11. PCA : Example ï‚› Which parameters can we ignore? ï‚› Constant parameter (number of heads) 1,1,...,1. ï‚› Constant parameter with some noise - (thickness of hair) 0.003, 0.005,0.002,....,0.0008 -> low variance ï‚› Parameter that is linearly dependent on other parameters (head size and height) Z= aX + bY
- 12. PCA : Example ï‚› Change of Basic
- 13. PCA : Example
- 14. PCA : Example
- 15. PCA : Example
- 16. PCA : Example
- 17. PCA : Example
- 18. PCA : Example
- 19. PCA : Example
- 20. PCA : Example
- 21. PCA : Example
- 22. PCA : Example
- 23. PCA : Example
- 24. PCA : Example
- 25. PCA : Example
- 26. Thank You