关键词: 异常值检测, 数据约简, 粒子群算法, 稀疏子空间

Abstract:

Aiming at the problem that outliers cannot be detected due to irrelevant attributes and redundant data in the high-dimensional data sets, a new Sparse Subspace-based algorithm for Local Outlier Detection（SSLOD）is proposed. Firstly, the outlier factor of the object is defined according to the local density of the object in each dimension. Secondly, the attributes unrelated to local outliers?and redundant objects in the data set are reduced based on the threshold of outlier factor. Finally, the improved particle swarm optimization algorithm is used to search sparse subspace in the simplified data set, and the local outliers are included in the sparse subspace. The effectiveness and accuracy of the proposed algorithm is demonstrated by the comprehensive experiments on synthetic and real-life data sets.

Key words: outlier detection, data reduction, particle swarm optimization, sparse subspace