Principal component analysis (PCA) with small number of observations

miguelcc
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Dear all,
I'd like to apply principal component analysis (PCA) to hyperspectral data (~1000 bands). The number of observations is 200.
The estimated variance covarance matrix is singular because the number of observations is smaller than the number of variables.

My questions are,

Can I still perform PCA (number of variables is < number of observations)?

Is the maximum number of meaninful principal components equal to 199?

Could you also provide me with references, please?

Thanks a lot in advance.

MiguelCC
 
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I am not sure but I would search for: "PCA in cluster analysis" since this is a method for dimension reduction of the phase space. Wikipedia has a good overview on PCA.
 

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