Linear-time One-Class Classification with Repeated Element-wise Folding
This provides a faster and easier-to-use alternative for one-class classification tasks, though it appears incremental as it builds on existing concepts with efficiency improvements.
The paper tackles the problem of computationally expensive one-class classification by proposing a linear-time method called Repeated Element-wise Folding (REF), which achieves similar or better performance than more complex algorithms on benchmark datasets.
This paper proposes an easy-to-use method for one-class classification: Repeated Element-wise Folding (REF). The algorithm consists of repeatedly standardizing and applying an element-wise folding operation on the one-class training data. Equivalent mappings are performed on unknown test items and the classification prediction is based on the item's distance to the origin of the final distribution. As all the included operations have linear time complexity, the proposed algorithm provides a linear-time alternative for the commonly used computationally much more demanding approaches. Furthermore, REF can avoid the challenges of hyperparameter setting in one-class classification by providing robust default settings. The experiments show that the proposed method can produce similar classification performance or even outperform the more complex algorithms on various benchmark datasets. Matlab codes for REF are publicly available at https://github.com/JenniRaitoharju/REF.