  Multi-view learning (MVL) has gained great success in integrating information
from multiple perspectives of a dataset to improve downstream task performance.
To make MVL methods more practical in an open-ended environment, this paper
investigates a novel paradigm called multi-view class incremental learning
(MVCIL), where a single model incrementally classifies new classes from a
continual stream of views, requiring no access to earlier views of data.
However, MVCIL is challenged by the catastrophic forgetting of old information
and the interference with learning new concepts. To address this, we first
develop a randomization-based representation learning technique serving for
feature extraction to guarantee their separate view-optimal working states,
during which multiple views belonging to a class are presented sequentially;
Then, we integrate them one by one in the orthogonality fusion subspace spanned
by the extracted features; Finally, we introduce selective weight consolidation
for learning-without-forgetting decision-making while encountering new classes.
Extensive experiments on synthetic and real-world datasets validate the
effectiveness of our approach.
