arXiv:2505.10882v2 Announce Type: replace Abstract: Principal component analysis classically requires full $d$-dimensional samples, yet in various applications hardware limits acquisition to a few scalar measurements per sample. We analyze a compressed variant of Oja's algorithm for estimating the principal eigenvector of the data covariance matrix using only two adaptive measurements per sample. At each iteration, we observe one measurement along the current estimate and one in a random orthogonal direction. We prove that after $t$ iterations, the expected sine-squared error to the true eigen
The paper was recently published (2026-06-02T04:00:00) on arXiv, indicating a current development in machine learning research.
This research provides a more efficient method for estimating principal eigenvectors, critical for various AI and data processing applications, by reducing required measurements.
The advancement allows for effective principal component analysis with significantly less data, potentially enabling AI deployment in hardware-constrained environments or with real-time adaptive sensing.
- · AI hardware manufacturers
- · Edge AI developers
- · Data-constrained sensor networks
- · Machine learning researchers
- · Traditional high-dimensional data processing techniques
More efficient and resource-friendly AI algorithms become available for deployment.
This efficiency could lead to the proliferation of AI in devices with limited computational or data acquisition capabilities.
The reduced data requirement might accelerate AI adoption in sectors where data privacy or bandwidth is a major concern, potentially fueling autonomous agents in sensitive environments.
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Read at arXiv cs.LG