A boosted outlier detection method based on the spectrum of the Laplacian matrix of a graph
This is an incremental improvement for data analysis, offering a more efficient outlier detection method for larger datasets.
The paper tackles outlier detection by proposing a new algorithm that uses the spectrum of the Laplacian matrix of a graph with boosting and sparse-data learners, resulting in reduced computational burden and competitive performance on synthetic datasets compared to methods like Isolation Forest and Local Outlier Factor.
This paper explores a new outlier detection algorithm based on the spectrum of the Laplacian matrix of a graph. Taking advantage of boosting together with sparse-data based learners. The sparcity of the Laplacian matrix significantly decreases the computational burden, enabling a spectrum based outlier detection method to be applied to larger datasets compared to spectral clustering. The method is competitive on synthetic datasets with commonly used outlier detection algorithms like Isolation Forest and Local Outlier Factor.