Learning features in deep architectures with unsupervised kernel k-means

Deep learning technology and related algorithms have dramatically broken landmark records for a broad range of learning problems in vision, speech, audio, and text processing. Meanwhile, kernel methods have found common-place usage due to their nonlinear expressive power and elegant optimization formulation. Based on recent progress in learning high-level, class-specific features in unlabeled data, we improve upon the result by combining nonlinear kernels and multi-layer (deep) architecture, which we apply at scale. In particular, our experimentation is based on k-means with an RBF kernel, though it is a straightforward extension to other unsupervised clustering techniques and other reproducing kernel Hilbert spaces. With the proposed method, we discover features distilled from unorganized images. We augment high-level feature invariance by pooling techniques.