文件名称:PCA
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Principal Component Analysis
• Uses:
– Data Visualization
– Data Reduction
– Data Classification
– Trend Analysis
– Factor Analysis
– Noise Reduction
• Examples:
– How many unique “sub-sets” are in
the sample?
– How are they similar / different?
– What are the underlying factors that
influence the samples?
– Which time / temporal trends are
(anti)correlated?
– Which measurements are needed to
differentiate?
– How to best present what is
“interesting”?
– Which “sub-set” does this new sample
rightfully belong?-Principal Component Analysis
• Uses:
– Data Visualization
– Data Reduction
– Data Classification
– Trend Analysis
– Factor Analysis
– Noise Reduction
• Examples:
– How many unique “sub-sets” are in
the sample?
– How are they similar/different?
– What are the underlying factors that
influence the samples?
– Which time/temporal trends are
(anti)correlated?
– Which measurements are needed to
differentiate?
– How to best present what is
“interesting”?
– Which “sub-set” does this new sample
rightfully belong?
• Uses:
– Data Visualization
– Data Reduction
– Data Classification
– Trend Analysis
– Factor Analysis
– Noise Reduction
• Examples:
– How many unique “sub-sets” are in
the sample?
– How are they similar / different?
– What are the underlying factors that
influence the samples?
– Which time / temporal trends are
(anti)correlated?
– Which measurements are needed to
differentiate?
– How to best present what is
“interesting”?
– Which “sub-set” does this new sample
rightfully belong?-Principal Component Analysis
• Uses:
– Data Visualization
– Data Reduction
– Data Classification
– Trend Analysis
– Factor Analysis
– Noise Reduction
• Examples:
– How many unique “sub-sets” are in
the sample?
– How are they similar/different?
– What are the underlying factors that
influence the samples?
– Which time/temporal trends are
(anti)correlated?
– Which measurements are needed to
differentiate?
– How to best present what is
“interesting”?
– Which “sub-set” does this new sample
rightfully belong?
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