Seo Hyeong Jeong

These projections result in a new space, where each basis vector encapsulates the most variance (i.e. the projections onto the eigenvector with the largest eigenvalue have the most variance, the ones on the second eigenvector have the second most variance, etc.). These new basis vectors are referred to as the principal components. We want principal componen…

…l, Principal Components Analysis (PCA) is also widely used as a dimensionality reduction technique. However, PCA maps the input in a different way than an Autoencoder.