query: Can PCA work when the number of observations is smaller than the number of dimensions?
positive_passages.docid: 154261
positive_passages.text: PCA when the dimensionality is greater than the number of samples
negative_passages.docid: 45832
negative_passages.text: What is the relation between k-means clustering and PCA?
negative_passages.docid: 18487
negative_passages.text: Why are there only $n-1$ principal components for $n$ data if the number of dimensions is $\ge n$?
negative_passages.docid: 52047
negative_passages.text: How can top principal components retain the predictive power on a dependent variable (or even lead to better predictions)?
negative_passages.docid: 26181
negative_passages.text: Using principal component analysis (PCA) for feature selection
negative_passages.docid: 22551
negative_passages.text: What can cause PCA to worsen results of a classifier?
negative_passages.docid: 125575
negative_passages.text: Dimensional regularization: removing more than just logarithmic divergencies?
negative_passages.docid: 6590
negative_passages.text: PCA and proportion of variance explained
negative_passages.docid: 16641
negative_passages.text: Strange results of varimax rotation of principal component analysis in Stata: rotated components are all zeros and ones
negative_passages.docid: 49766
negative_passages.text: Examples of results failing in higher dimensions
negative_passages.docid: 28133
negative_passages.text: Theoretical: Minimum Number of Support Vectors
negative_passages.docid: 84446
negative_passages.text: How to project a new vector onto PCA space?
negative_passages.docid: 110802
negative_passages.text: Model selection with low N?
negative_passages.docid: 7632
negative_passages.text: Interpretation of biplots in principal components analysis
negative_passages.docid: 55847
negative_passages.text: PCA demeaning the data
negative_passages.docid: 14181
negative_passages.text: Can averaging all the variables be seen as a crude form of PCA?
negative_passages.docid: 126136
negative_passages.text: Fundamental question about dimensional analysis