Paper:
An Empirical Study on The Properties of Random Bases for Kernel Methods
Kernel machines as well as neural networks possess universal function approximation properties. Nevertheless in practice their ways of choosing the appropriate function class differ. Specifically neural networks learn a representation by adapting their basis functions to the data and the task at hand, while kernel methods typically use a basis that is not adapted during training. In this work, we contrast random features of approximated kernel machines with learned features of neural networks. Our analysis reveals how these random and adaptive basis functions affect the quality of learning. Furthermore, we present basis adaptation schemes that allow for a more compact representation, while retaining the generalization properties of kernel machines.

Review:
Summary:
The authors provided an empirical study contrasting neural networks and kernel methods, with a focus on how random and adaptive schemes would make efficient use of features in order to improve quality of learning, at four levels of abstraction: data-agnostic random basis (baseline kernel machines with traditional random features), unsupervised data-adaptive basis for better approximation of kernel function, supervised data-label-adaptive basis by kernel target alignment, discriminatively adaptive basis (neural nets). The paper concluded with several suggestions and caveats for efficient use of random features in practice.
Comments:
- 1 -
Line 123, especially for sake of comparing UAB case where the underlying assumption is that using the true kernel function k in prediction yields the "best" performance so that UAB tries to approximate it, I would suggest testing in experiments a baseline model that utilizes the true kernel function k in prediction. Also this would suggest, for example in Fig. 1 at which point of the KAE curve the accuracy is sufficiently good (despite many theoretical results available).
- 2 -
The four datasets chosen in the paper certainly demonstrate proof for conclusions finally drawn. However, in order to support those conclusions to a definitively convincing extent, more datasets should be needed. For example, the performance scores in Tab. 1 do not seem to be too significantly different marginally for each task. And reasons for inconsistent behaviors across different tasks (CoverType vs others in Fig. 1 for instance, Line 222) are not well explained through empirical exploration.
- 3 -
It is not clear how big the difference can be between the four adaptive schemes in terms of training time, which can be crucial in practice. In addition, as the number of (approximate) features D increases, how does the training time increase accordingly in general for each scheme empirically? It would be interesting to also report this.