SIM: Open-World Multi-Task Stream Classifier with Integral Similarity Metrics

One of the key challenges of performing label predictions over a data stream is concerned with the emergence of instances belonging to unobserved (or novel) classes over time. Although existing studies have proposed various solutions to address this challenge, they mostly focus on streams with lowdimensional data and strongly rely on the intrinsic cohesion and separation data property, i.e., instances belonging to the same class are closer to each other (cohesion) than those belonging to different classes (separation) in the observed feature space, to detect instances from unknown classes. Unfortunately, such a property is typically not inherent in high-dimensional data such as images and texts. Thus, to perform classification and novel class detection on high-dimensional data streams, we need to address two main problems: 1) Finding a feature space that exhibit cohesion and separation properties, and 2) Training with limited amount of labeled data. In this paper, we propose a multi-task metric learning mechanism useful for identifying a latent space in which the cohesion and separation data property is valid and have designed a semi-supervised stream classifier called SIM based on this mechanism. We empirically measure the performance of SIM over multiple real-world image and text datasets, and demonstrate its superiority by comparing the performance with existing state-of-the-art frameworks.