principal component analysis-presentation.ppt

7/28/2019 Principal Component Analysis-PRESENTATION.ppt

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Principal Component Analysis(PCA)

Inter correlated quantitativedependent variables

Extract the important information fromthe prepared table of data, to represent

it as a set of new orthogonal variablescalled principal components


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Objectives of principal component

analysis

To discover or to reduce the dimensionality of

the data set. To identify new meaningful underlying variables.


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Principal of PCA

It reduces the variability in the data by

creating the artificial variables that are calledprinciple components

Projects a dataset to a new coordinate

system by determining the eigenvectors and

eigenvalues of a matrix


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Example

1.My supervisor treats me with consideration.

2. My supervisor consults me concerning important decisions that

affect my work.3. My supervisors give me recognition when I do a good job.

4. My supervisor gives me the support I need to do my job

Well.

5. My pay is fair.

6. My pay is appropriate, given the amount of responsibility thatcomes with my job.

7. My pay is comparable to the pay earned by other employeeswhose jobs are similar to mine.


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Applications of PCA

Neuroscience

Computer graphics Meteorology

Oceanography

Gene expression


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Application of PCA in Neuroscience

Quantitative analysis of

minimal Excitatory

Postsynaptic Potentials

(EPSPs)


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Application of PCA in Computer

graphics

Compression of Bidirectional Texture

Functions (BTF)


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Application of PCA in Metrology

Daily temperature variation (1998-2000)


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Application of PCA in Metrology


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Application of PCA in Oceanography

43 stations in a study about the atmospheric

pressure and mean seasonal cycle


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Application of PCA in Gene Expression


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Conclusion

By: Ayesha Yousaf

principal component analysis-presentation.ppt

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