Outlier detection: How to Select k for k-nearest-neighbors-based outlier detectors
Unsupervised k-nearest-neighbor-based outlier detectors play a vital role in data science
research. However, the detectors' performance relies on the choice of the parameter k …
research. However, the detectors' performance relies on the choice of the parameter k …
[HTML][HTML] Neighborhood representative for improving outlier detectors
Over the decades, traditional outlier detectors have ignored the group-level factor when
calculating outlier scores for objects in data by evaluating only the object-level factor, failing …
calculating outlier scores for objects in data by evaluating only the object-level factor, failing …
Outlier detection method based on high-density iteration
Y Zhou, H Xia, D Yu, J Cheng, J Li - Information Sciences, 2024 - Elsevier
In conventional outlier detection, global outliers are easily identified, but the efficacy
diminishes when faced with local outliers within clusters of varying densities. Conversely …
diminishes when faced with local outliers within clusters of varying densities. Conversely …
Smoothing Outlier Scores is All You Need to Improve Outlier Detectors
We hypothesize that similar objects should have similar outlier scores. To the best of our
knowledge, all existing outlier detectors calculate the outlier score for each object …
knowledge, all existing outlier detectors calculate the outlier score for each object …
Neighborhood averaging for improving outlier detectors
We hypothesize that similar objects should have similar outlier scores. To our knowledge, all
existing outlier detectors calculate the outlier score for each object independently regardless …
existing outlier detectors calculate the outlier score for each object independently regardless …
Unbiased Anomalous Trajectory Detection with Hierarchical Sequence Modeling
X Kong, Y He, G Shen, J Du, Z Liu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Anomalous trajectory detection plays an important role in the field of trajectory big data
mining, providing significant support for identifying drivers traveling at inappropriate speeds …
mining, providing significant support for identifying drivers traveling at inappropriate speeds …
Improving autoencoder-based outlier detection with adjustable probabilistic reconstruction error and mean-shift outlier scoring
Autoencoders were widely used in many machine learning tasks thanks to their strong
learning ability which has drawn great interest among researchers in the field of outlier …
learning ability which has drawn great interest among researchers in the field of outlier …
Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection
Deep learning-based sequence models are extensively employed in Time Series Anomaly
Detection (TSAD) tasks due to their effective sequential modeling capabilities. However, the …
Detection (TSAD) tasks due to their effective sequential modeling capabilities. However, the …
Weakly-supervised anomaly detection for multimodal data distributions
Weakly-supervised anomaly detection can outperform existing unsupervised methods with
the assistance of a very small number of labeled anomalies, which attracts increasing …
the assistance of a very small number of labeled anomalies, which attracts increasing …
Decompose, Attend and Detect: A Robust Framework for Time Series Anomaly Detection
Deep learning-based reconstruction frameworks are widely employed in Time Series
Anomaly Detection (TSAD) tasks. However, these detectors encounter challenges in …
Anomaly Detection (TSAD) tasks. However, these detectors encounter challenges in …