Outlier Detection in High Dimensional Data

By Charu C. Aggarwal, Philip S. Yu. Regular Issue, 2021

571 citations recorded by the source corpus.

Artificial intelligence (AI) is the science that allows computers to replicate human intelligence in areas such as decision-making, text processing, visual perception. Artificial Intelligence is the broader field that contains several subfields such as machine learning, robotics, and computer vision. Machine Learning is a branch of Artificial Intelligence that allows a machine to learn and improve at a task over time. Deep Learning is a subset of machine learning that makes use of deep artificial neural networks for training. The paper proposed on outlier detection for multivariate high dimensional data for Autoencoder unsupervised model.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4a6e74d4bf4fd0106891e5518692a77c7aa8811d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dawn Song
2. Hui Xiong
3. Jia Wu
4. Quan Z. Sheng
5. Chuan Zhou
6. Min Du
7. Shan Xue
8. Xiaoxiao Ma

In Computer Vision and Pattern Recognition

1. Dawn Song
2. Hui Xiong
3. Jia Wu
4. Quan Z. Sheng
5. Chuan Zhou
6. Min Du
7. Ruoxi Jia
8. Peng Ning

In Radiology, Nuclear Medicine and Imaging

1. Dawn Song

Citations in the corpus

• Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy (2019, background): “Outlier detection is the process of identifying rare items in a dataset that significantly differ from the majority ([22]), while novelty detection is to detect new observations that lie in the low density area of the existing dataset ([23, 24]).”
• A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, methodology): “By pairing the conventional anomaly detection techniques such as density-based techniques [86] and distance-based techniques [87] with node embedding techniques, anomalous nodes can be identified with regard to their distinguishable locations (i.e., low-density areas or far away from the majorities)…”
• White Rose Consortium ePrints Repository (unknown): “Aggarwal (Aggarwal and Yu, 2001) notes that outliers may be considered as noise points lying outside a set of defined clusters or alternatively outliers may be defined as the points that lie outside of the set of clusters but are also separated from the noise.”