Principal components: A descent algorithm

Many frequently arising problems involve finding the small-dimensional subspace that best captures the variablity of a set of observations belonging to a larger space, for example, finding its principal components. We propose an algorithm that finds this subspace through a series of orthogonal rotations, each represented as the exponential of a skew-symmetric matrix picked such that we minimize the cost function associated to our problem.