Skip to main content
Cross Validated

Return to Question

added 1 character in body
Source Link
jav321
jav321
  • 13
  • 3

I have a data set, I calculate the correlation matrix and get eigen values for PCA. I want to intuitively understand following features

  1. Number of significant eigenvalues. In my dataset, some vectorsmatrices have only 3 or 4 significant eigenvalues(the value drops from like 140-200 to 1). However some have relatively large number of significant eigenvalues. After 20-50 eigenvalues, the magnitude drops to 1. What is this telling me ?

  2. Magnitude of first few eigenvalues. If I take 3-4 largest values and some data subset have large eigen values and some have less, what is this telling me ?

I have a data set, I calculate the correlation matrix and get eigen values for PCA. I want to intuitively understand following features

  1. Number of significant eigenvalues. In my dataset, some vectors have only 3 or 4 significant eigenvalues(the value drops from like 140-200 to 1). However some have relatively large number of significant eigenvalues. After 20-50 eigenvalues, the magnitude drops to 1. What is this telling me ?

  2. Magnitude of first few eigenvalues. If I take 3-4 largest values and some data subset have large eigen values and some have less, what is this telling me ?

I have a data set, I calculate the correlation matrix and get eigen values for PCA. I want to intuitively understand following features

  1. Number of significant eigenvalues. In my dataset, some matrices have only 3 or 4 significant eigenvalues(the value drops from like 140-200 to 1). However some have relatively large number of significant eigenvalues. After 20-50 eigenvalues, the magnitude drops to 1. What is this telling me ?

  2. Magnitude of first few eigenvalues. If I take 3-4 largest values and some data subset have large eigen values and some have less, what is this telling me ?

Source Link
jav321
jav321
  • 13
  • 3

PCA features intuition : number/decay of eigenvalues

I have a data set, I calculate the correlation matrix and get eigen values for PCA. I want to intuitively understand following features

  1. Number of significant eigenvalues. In my dataset, some vectors have only 3 or 4 significant eigenvalues(the value drops from like 140-200 to 1). However some have relatively large number of significant eigenvalues. After 20-50 eigenvalues, the magnitude drops to 1. What is this telling me ?

  2. Magnitude of first few eigenvalues. If I take 3-4 largest values and some data subset have large eigen values and some have less, what is this telling me ?