Randomized PCA Forest for Outlier Detection
This addresses outlier detection for data analysis applications, but it appears incremental as it adapts an existing technique (Randomized PCA Forest) to a new task.
The paper tackles unsupervised outlier detection by proposing a method based on Randomized PCA Forest, which shows superiority over classical and state-of-the-art methods on several datasets and competitive performance on others, with high generalization power and computational efficiency.
We propose a novel unsupervised outlier detection method based on Randomized Principal Component Analysis (PCA). Inspired by the performance of Randomized PCA (RPCA) Forest in approximate K-Nearest Neighbor (KNN) search, we develop a novel unsupervised outlier detection method that utilizes RPCA Forest for outlier detection. Experimental results showcase the superiority of the proposed approach compared to the classical and state-of-the-art methods in performing the outlier detection task on several datasets while performing competitively on the rest. The extensive analysis of the proposed method reflects it high generalization power and its computational efficiency, highlighting it as a good choice for unsupervised outlier detection.