Revisiting Non-separable Binary Classification and its Applications in Anomaly Detection

2023-12-03 · Matthew Lau, Ismaila Seck, Athanasios P Meliopoulos, Wenke Lee, Eugene Ndiaye

The inability to linearly classify XOR has motivated much of deep learning. We revisit this age-old problem and show that linear classification of XOR is indeed possible. Instead of separating data between halfspaces, we propose a slightly different paradigm, equality separation, that adapts the SVM objective to distinguish data within or outside the margin. Our classifier can then be integrated into neural network pipelines with a smooth approximation. From its properties, we intuit that equality separation is suitable for anomaly detection. To formalize this notion, we introduce closing numbers, a quantitative measure on the capacity for classifiers to form closed decision regions for anomaly detection. Springboarding from this theoretical connection between binary classification and anomaly detection, we test our hypothesis on supervised anomaly detection experiments, showing that equality separation can detect both seen and unseen anomalies.

📄 PDF Abstract BibTeX arXiv:2312.01541

Code (1)

mattlaued/xor-is-linearly-classifiable 공식 구현 tf

Tasks

Anomaly Detection Binary Classification Supervised Anomaly Detection

Methods 이 논문이 사용한 방법론

SVM A Support Vector Machine, or SVM, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs…

Application of Unsupervised Domain Adaptation for Structural MRI Analysis

2022-12-26 · Pranath Reddy

The primary goal of this work is to study the effectiveness of an unsupervised domain adaptation approach for various applications such as binary classification and anomaly detection in the context of Alzheimer's disease…

Anomaly Detection Binary Classification Classification Domain Adaptation+3

Augment to Detect Anomalies with Continuous Labelling

2022-07-03 · Vahid Reza Khazaie, Anthony Wong, Yalda Mohsenzadeh

Anomaly detection is to recognize samples that differ in some respect from the training observations. These samples which do not conform to the distribution of normal data are called outliers or anomalies. In real-world …

Anomaly Detection Image Augmentation

Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data

2024-12-16 · Mulugeta Weldezgina Asres, Christian Walter Omlin, the CMS-HCAL Collaboration

Extracting anomaly causality facilitates diagnostics once monitoring systems detect system faults. Identifying anomaly causes in large systems involves investigating a more extensive set of monitoring variables across mu…

Causal Discovery Computational Efficiency

SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification

2023-04-30 · Minghui Yang, Jing Liu, Zhiwei Yang, Zhaoyang Wu

Industrial image anomaly detection under the setting of one-class classification has significant practical value. However, most existing models struggle to extract separable feature representations when performing featur…

Anomaly Detection Classification One-Class Classification Self-Supervised Learning

Continual Learning in Linear Classification on Separable Data

2023-06-06 · Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh 외

We analyze continual learning on a sequence of separable linear classification tasks with binary labels. We show theoretically that learning with weak regularization reduces to solving a sequential max-margin problem, co…

Classification Continual Learning regression Scheduling