It means the original data was barely three-dimensional; the data cloud hovered extremely close to the 2D projection plane. Squishing it flat cost almost nothing because there was almost no depth or thickness to lose in the discarded dimensions.
Conditions: The data is projected onto a lower-dimensional subspace.; The variance explained is calculated based on the eigenvalues of the principal components.
It means the original data was barely three-dimensional; the data cloud hovered extremely close to the 2D projection plane. Squishing it flat cost almost nothing because there was almost no depth or thickness to lose in the discarded dimensions.
Conditions: The data is projected onto a lower-dimensional subspace.; The variance explained is calculated based on the eigenvalues of the principal components.