OCmst: One-class Novelty Detection using Convolutional Neural Network and Minimum Spanning Trees
This addresses novelty detection for scenarios with outlier-free training data, offering a domain-specific improvement.
The authors tackled novelty detection by proposing OCmst, a model combining CNN feature extraction with Minimum Spanning Trees, achieving state-of-the-art results on the CIFAR10 dataset.
We present a novel model called One Class Minimum Spanning Tree (OCmst) for novelty detection problem that uses a Convolutional Neural Network (CNN) as deep feature extractor and graph-based model based on Minimum Spanning Tree (MST). In a novelty detection scenario, the training data is no polluted by outliers (abnormal class) and the goal is to recognize if a test instance belongs to the normal class or to the abnormal class. Our approach uses the deep features from CNN to feed a pair of MSTs built starting from each test instance. To cut down the computational time we use a parameter $γ$ to specify the size of the MST's starting to the neighbours from the test instance. To prove the effectiveness of the proposed approach we conducted experiments on two publicly available datasets, well-known in literature and we achieved the state-of-the-art results on CIFAR10 dataset.