Principal component Analysis (PCA) ThePCA is a method for dimension reduction. It projects the original variables in the direction that maximizes the variance.
In our figure, the red points come from a bivariate normal distribution. The vectors are the eigenvectors and the size of these vectors are proportional to the values of the respective eigenvalues. Principal component analysis provides new directions that are orthogonal and point to the directions of high variance.
Principal component Analysis The red points come from a bivariate normal distribution. The vectors are the eigenvectors and the size of these vectors are proportional to the values of the respective eigenvalues. Principal component analysis provides new directions that are orthogonal and point to the directions of high variance.
Principal component Analysis (PCA) PCA is a method for dimension reduction. It projects the original variables in the direction that maximizes the variance.
In our figure, the red points come from a bivariate normal distribution. The vectors are the eigenvectors and the size of these vectors are proportional to the values of the respective eigenvalues. Principal component analysis provides new directions that are orthogonal and point to the directions of high variance.
Principal component Analysis The red points come from a bivariate normal distribution. The vectors are the eigenvectors and the size of these vectors are proportional to the values of the respective eigenvalues. Principal component analysis provides new directions that are orthogonal and point to the directions of high variance.
