# Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure

arXiv: https://arxiv.org/abs/2007.10088 · HTML: https://mlsanity.com/paper/2007.10088/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2020.

## Built on by (most cited first, 27 of 27)

- [Rethinking Graph Neural Networks for Anomaly Detection](https://mlsanity.com/paper/2205.15508/) (2022)
- [LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks](https://mlsanity.com/paper/2112.05355/) (2021)
- [A Survey on Explainable Anomaly Detection](https://mlsanity.com/paper/2210.06959/) (2022)
- [Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filtering](https://mlsanity.com/paper/2106.07992/) (2021)
- [Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks](https://mlsanity.com/paper/2303.01978/) (2023)
- [Weakly Supervised Anomaly Detection via Knowledge-Data Alignment](https://mlsanity.com/paper/2402.03785/) (2024)
- [A general-purpose method for applying Explainable AI for Anomaly Detection](https://mlsanity.com/paper/2207.11564/) (2022)
- [Anomaly Attribution with Likelihood Compensation](https://mlsanity.com/paper/2208.10679/) (2022)
- [On Root Cause Localization and Anomaly Mitigation through Causal Inference](https://mlsanity.com/paper/2212.04031/) (2022)
- [Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attribution](https://mlsanity.com/paper/2308.04708/) (2023)
- [Trustworthy Anomaly Detection: A Survey](https://mlsanity.com/paper/2202.07787/) (2022)
- [Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators](https://mlsanity.com/paper/2101.04645/) (2021)
- [Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach](https://mlsanity.com/paper/2308.01063/) (2023)
- [Revisiting Non-separable Binary Classification and its Applications in Anomaly Detection](https://mlsanity.com/paper/2312.01541/) (2023)
- [Random Subspace Mixture Models for Interpretable Anomaly Detection](https://mlsanity.com/paper/2108.06283/) (2021)
- [OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary](https://mlsanity.com/paper/2110.15273/) (2021)
- [NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction](https://mlsanity.com/paper/2203.08339/) (2022)
- [StreaMulT: Streaming Multimodal Transformer for Heterogeneous and Arbitrary Long Sequential Data](https://mlsanity.com/paper/2110.08021/) (2021)
- [Improving Novelty Detection using the Reconstructions of Nearest Neighbours](https://mlsanity.com/paper/2111.06150/) (2021)
- [Learning Invariant Rules from Data for Interpretable Anomaly Detection](https://mlsanity.com/paper/2211.13577/) (2022)
- [Black-Box Anomaly Attribution](https://mlsanity.com/paper/2305.18440/) (2023)
- [AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection](https://mlsanity.com/paper/2405.03075/) (2024)
- [Interactive System-wise Anomaly Detection](https://mlsanity.com/paper/2304.10704/) (2023)
- [Boundary Peeling: Outlier Detection Method Using One-Class Peeling](https://mlsanity.com/paper/2309.05630/) (2023)
- [Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies](https://mlsanity.com/paper/2506.13955/) (2025)
- [Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference](https://mlsanity.com/paper/2604.13252/) (2026)
- [BREAD: Baseline-Referenced Explanations for Anomaly Diagnosis](https://mlsanity.com/paper/2608.10587/) (2026)

## Abstract (arXiv)

Complex devices are connected daily and eagerly generate vast streams of multidimensional state measurements. These devices often operate in distinct modes based on external conditions (day/night, occupied/vacant, etc.), and to prevent complete or partial system outage, we would like to recognize as early as possible when these devices begin to operate outside the normal modes. Unfortunately, it is often impractical or impossible to predict failures using rules or supervised machine learning, because failure modes are too complex, devices are too new to adequately characterize in a specific environment, or environmental change puts the device into an unpredictable condition. We propose an unsupervised anomaly detection method that creates a negative sample from the positive, observed sample, and trains a classifier to distinguish between positive and negative samples. Using the Contraction Principle, we explain why such a classifier ought to establish suitable decision boundaries between normal and anomalous regions, and show how Integrated Gradients can attribute the anomaly to specific variables within the anomalous state vector. We have demonstrated that negative sampling with random forest or neural network classifiers yield significantly higher AUC scores than Isolation Forest, One Class SVM, and Deep SVDD, against (a) a synthetic dataset with dimensionality ranging between 2 and 128, with 1, 2, and 3 modes, and with and without noise dimensions; (b) four standard benchmark datasets; and (c) a multidimensional, multimodal dataset from real climate control devices. Finally, we describe how negative sampling with neural network classifiers have been successfully deployed at large scale to predict failures in real time in over 15,000 climate-control and power meter devices in 145 Google office buildings.