Density-preserving projections for large-scale local anomaly detection

作者：de Vries Timothy^*; Chawla Sanjay; Houle Michael E

来源：Knowledge and Information Systems, 2012, 32(1): 25-52.

DOI：10.1007/s10115-011-0430-4

• Anomaly detection
• Dimensionality reduction

Outlier or anomaly detection is a fundamental data mining task with the aim to identify data points, events, transactions which deviate from the norm. The identification of outliers in data can provide insights about the underlying data generating process. In general, outliers can be of two kinds: global and local. Global outliers are distinct with respect to the whole data set, while local outliers are distinct with respect to data points in their local neighbourhood. While several approaches have been proposed to scale up the process of global outlier discovery in large databases, this has not been the case for local outliers. We tackle this problem by optimising the use of local outlier factor (LOF) for large and high-dimensional data. We propose projection-indexed nearest-neighbours (PINN), a novel technique that exploits extended nearest-neighbour sets in a reduced-dimensional space to create an accurate approximation for k-nearest-neighbour distances, which is used as the core density measurement within LOF. The reduced dimensionality allows for efficient sub-quadratic indexing in the number of items in the data set, where previously only quadratic performance was possible. A detailed theoretical analysis of random projection (RP) and PINN shows that we are able to preserve the density of the intrinsic manifold of the data set after projection. Experimental results show that PINN outperforms the standard projection methods RP and PCA when measuring LOF for many high-dimensional real-world data sets of up to 300,000 elements and 102,600 dimensions. A further investigation into the use of high-dimensionality-specific indexing such as spatial approximate sample hierarchy (SASH) shows that our novel technique holds benefits over even these types of highly efficient indexing. We cement the practical applications of our novel technique with insights into what it means to find local outliers in real data including image and text data, and include potential applications for this knowledge.

• 出版日期2012-7