Atom dimension adaptation for infinite set dictionary learning
This work addresses anomaly detection in signal processing by enhancing dictionary learning techniques, though it appears incremental as it builds on existing set-atom methods.
The paper tackles the problem of adaptively adjusting set-atom sizes in dictionary learning to match their contribution in representing signals, resulting in decreased representation error and improved anomaly detection performance for 'dependency' anomalies, outperforming state-of-the-art methods.
Recent work on dictionary learning with set-atoms has shown benefits in anomaly detection. Instead of viewing an atom as a single vector, these methods allow building sparse representations with atoms taken from a set around a central vector; the set can be a cone or may have a probability distribution associated to it. We propose a method for adaptively adjusting the size of set-atoms in Gaussian and cone dictionary learning. The purpose of the algorithm is to match the atom sizes with their contribution in representing the signals. The proposed algorithm not only decreases the representation error, but also improves anomaly detection, for a class of anomalies called `dependency'. We obtain better detection performance than state-of-the-art methods.