What Is the Interpretation of Principal Components in Principal Component Analysis?
In Principal Component Analysis (PCA), the "principal components" refer to orthogonal linear combinations of the original variables that successively account for the maximum possible variance in the dataset.
The first principal component accounts for the largest possible variance in the data, projected along its corresponding direction.
The second principal component accounts for the largest remaining variance and is mathematically orthogonal to the first principal component.
This process continues iteratively, either until the total variance in the data is fully accounted for or until a predetermined number of principal components is obtained.
Each principal component is associated with an eigenvector (indicating its direction) and an eigenvalue (representing the amount of variance it explains). The outcome of PCA is typically expressed in terms of these eigenvectors and eigenvalues.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
Related Services
How to order?
