Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data
This addresses the challenge of training expressive models without overfitting in domains like neuroscience and medical imaging, though it is incremental as it builds on existing regularization ideas with a novel stochastic implementation.
The paper tackles the problem of overfitting in high-dimensional, small-sample datasets by proposing a feature grouping regularizer that leverages known feature correlations, and demonstrates improved generalization and faster convergence on fMRI and Olivetti Faces datasets.
In many applications where collecting data is expensive, for example neuroscience or medical imaging, the sample size is typically small compared to the feature dimension. It is challenging in this setting to train expressive, non-linear models without overfitting. These datasets call for intelligent regularization that exploits known structure, such as correlations between the features arising from the measurement device. However, existing structured regularizers need specially crafted solvers, which are difficult to apply to complex models. We propose a new regularizer specifically designed to leverage structure in the data in a way that can be applied efficiently to complex models. Our approach relies on feature grouping, using a fast clustering algorithm inside a stochastic gradient descent loop: given a family of feature groupings that capture feature covariations, we randomly select these groups at each iteration. We show that this approach amounts to enforcing a denoising regularizer on the solution. The method is easy to implement in many model architectures, such as fully connected neural networks, and has a linear computational cost. We apply this regularizer to a real-world fMRI dataset and the Olivetti Faces datasets. Experiments on both datasets demonstrate that the proposed approach produces models that generalize better than those trained with conventional regularizers, and also improves convergence speed.