Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A General Framework for Increasing the Robustness of PCA-Based Correlation Clustering Algorithms
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
A Unified View of Matrix Factorization Models
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
ACM Computing Surveys (CSUR)
Direct Robust Matrix Factorizatoin for Anomaly Detection
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Density-preserving projections for large-scale local anomaly detection
Knowledge and Information Systems
Outlier Ranking via Subspace Analysis in Multiple Views of the Data
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
| Hi-index | 0.00 |
The identification of outliers is an intrinsic component of knowledge discovery. However, most outlier detection techniques operate in the observational space, which is often associated with information redundancy and noise. Also, due to the usually high dimensionality of the observational space, the anomalies detected are difficult to comprehend. In this paper we claim that algorithms for discovery of outliers in a latent space will not only lead to more accurate results but potentially provide a natural medium to explain and describe outliers. Specifically, we propose combining Non-Negative Matrix Factorization (NMF) with subspace analysis to discover and interpret outliers. We report on preliminary work towards such an approach.