$\sigma$-PCA: a building block for neural learning of identifiable linear transformations

Fahdi Kanavati, Lucy Katsnith, Masayuki Tsuneki

Linear principal component analysis (PCA) learns (semi-)orthogonal transformations by orienting the axes to maximize variance. Consequently, it can only identify orthogonal axes wh…