Eigen-Component Analysis: A Quantum Theory-Inspired Linear Model

In modern machine learning circuits and systems, the need for centralized, standardized data poses significant challenges, especially in privacy-sensitive, resource-constrained, real-time, or asynchronous environments. We introduce Eigen-Component Analysis (ECA), a quantum-inspired linear model that circumvents these requirements by leveraging intrinsic data structures. ECA uses an orthogonal transformation to extract meaningful features from decentralized, non-standardized data, ensuring high classification accuracy without extensive preprocessing. Unlike traditional linear models, ECA adapts to complex data distributions and shows superior performance in feature extraction and classification across various datasets. Experiments indicate that ECA achieves robust accuracy with fewer parameters, highlighting its potential where data centralization and standardization are impractical. Our work extends the feasibility of linear models in diverse scenarios, enhancing interpretability and efficiency in challenging data environments.