# PCA-Driven Adaptive Sensor Triage for Edge AI Inference

arXiv: https://arxiv.org/abs/2604.05045 · HTML: https://mlsanity.com/paper/2604.05045/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 6 Apr 2026.

## Builds on (most cited first, 7 of 7)

- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Edge AI: A Taxonomy, Systematic Review and Future Directions](https://mlsanity.com/paper/2407.04053/) (2024)
- [Streaming PCA and Subspace Tracking: The Missing Data Case](https://mlsanity.com/paper/1806.04609/) (2018)
- [History PCA: A New Algorithm for Streaming PCA](https://mlsanity.com/paper/1802.05447/) (2018)
- [Scalable and Accurate Online Feature Selection for Big Data](https://mlsanity.com/paper/1511.09263/) (2015)
- [Low-Precision Streaming PCA](https://mlsanity.com/paper/2510.22440/) (2025)
- [Energy-Aware Adaptive Sampling for Self-Sustainability in Resource-Constrained IoT Devices](https://mlsanity.com/paper/2310.20331/) (2023)

## Abstract (arXiv)

Multi-channel sensor networks in industrial IoT often exceed available bandwidth. We propose PCA-Triage, a streaming algorithm that converts incremental PCA loadings into proportional per-channel sampling rates under a bandwidth budget. PCA-Triage runs in O(wdk) time with zero trainable parameters (0.67 ms per decision). We evaluate on 7 benchmarks (8--82 channels) against 9 baselines. PCA-Triage is the best unsupervised method on 3 of 6 datasets at 50% bandwidth, winning 5 of 6 against every baseline with large effect sizes (r = 0.71--0.91). On TEP, it achieves F1 = 0.961 +/- 0.001 -- within 0.1% of full-data performance -- while maintaining F1 > 0.90 at 30% budget. Targeted extensions push F1 to 0.970. The algorithm is robust to packet loss and sensor noise (3.7--4.8% degradation under combined worst-case).