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A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data
Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in h...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5706085/ https://www.ncbi.nlm.nih.gov/pubmed/29270197 http://dx.doi.org/10.1155/2017/8501683 |