Title: Learning Robust Representations for Computer Vision,
Abstract: Unsupervised learning techniques in computer vision often require learning
latent representations, such as low-dimensional linear and non-linear
subspaces. Noise and outliers in the data can frustrate these approaches by
obscuring the latent spaces.
Our main goal is deeper understanding and new development of robust
approaches for representation learning. We provide a new interpretation for
existing robust approaches and present two specific contributions: a new robust
PCA approach, which can separate foreground features from dynamic background,
and a novel robust spectral clustering method, that can cluster facial images
with high accuracy. Both contributions show superior performance to standard
methods on real-world test sets.