The study of representations invariant to common transformations of the data
is important to learning. Most techniques have focused on local approximate
invariance implemented within expensive optimization frameworks lacking
explicit theoretical guarantees. In this paper, we study kernels that are
invariant to a unitary group while having theoretical guarantees in addressing
the important practical issue of unavailability of transformed versions of
labelled data. A problem we call the Unlabeled Transformation Problem which is
a special form of semi-supervised learning and one-shot learning. We present a
theoretically motivated alternate approach to the invariant kernel SVM based on
which we propose Max-Margin Invariant Features (MMIF) to solve this problem. As
an illustration, we design an framework for face recognition and demonstrate
the efficacy of our approach on a large scale semi-synthetic dataset with
153,000 images and a new challenging protocol on Labelled Faces in the Wild
(LFW) while out-performing strong baselines.

本文研究了对称群不变的核技术，并提出了一套基于最大边界不变特征的解决方案来解决半监督学习和单样本学习的特殊形式的问题，这被称为未标记变换问题。 作为一个插图，我们设计了一个人脸识别框架，并在一个大规模半合成数据集和一个新的具有挑战性的 LFW 协议上论证了我们的方法的有效性，表现优于强基线。