Ordering as privileged information
This work addresses the challenge of faster convergence in machine learning for pattern recognition tasks, offering a novel method that could benefit researchers and practitioners in optimization and learning theory, though it appears incremental as it builds on existing LUPI frameworks.
The paper tackles the problem of accelerating convergence in pattern recognition by minimizing variance diameters of hypothesis spaces using a new order metric, which is framed as an ordinal regression problem and applied in a Learning Using Privileged Information (LUPI) context, with data experiments showing improved convergence rates.
We propose to accelerate the rate of convergence of the pattern recognition task by directly minimizing the variance diameters of certain hypothesis spaces, which are critical quantities in fast-convergence results.We show that the variance diameters can be controlled by dividing hypothesis spaces into metric balls based on a new order metric. This order metric can be minimized as an ordinal regression problem, leading to a LUPI (Learning Using Privileged Information) application where we take the privileged information as some desired ordering, and construct a faster-converging hypothesis space by empirically restricting some larger hypothesis space according to that ordering. We give a risk analysis of the approach. We discuss the difficulties with model selection and give an innovative technique for selecting multiple model parameters. Finally, we provide some data experiments.