Comparison theorems on large-margin learning

Notes on Margin Training and Margin p-Values for Deep Neural Network Classifiers

We provide a new local class-purity theorem for Lipschitz continuous DNN classifiers. In addition, we discuss how to achieve classification margin for training samples. Finally, we describe how to compute margin p-values for test samples.

Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin

In the absence of prior knowledge, ordinal embedding methods obtain new representation for items in a low-dimensional Euclidean space via a set of quadruple-wise comparisons. These ordinal comparisons often come from human annotators, and sufficient comparisons induce the success of classical approaches. However, collecting a large number of labeled data is known as a hard task, and most of the existing work pay little attention to the generalization ability with insufficient samples. Meanwhile, recent progress in large margin theory discloses that rather than just maximizing the minimum margi…

Large Margin Learning in Set to Set Similarity Comparison for Person Re-identification

Person re-identification (Re-ID) aims at matching images of the same person across disjoint camera views, which is a challenging problem in multimedia analysis, multimedia editing and content-based media retrieval communities. The major challenge lies in how to preserve similarity of the same person across video footages with large appearance variations, while discriminating different individuals. To address this problem, conventional methods usually consider the pairwise similarity between persons by only measuring the point to point (P2P) distance. In this paper, we propose to use deep learn…

迈向更深入的几何、分析和算法对边界的理解

Towards A Deeper Geometric, Analytic and Algorithmic Understanding of Margins

本文研究了矩阵A的边界条件度量，探讨了线性可行性问题的难度，通过几何、分析和算法方法扩展了边界理论，并证明了感知机收敛率与边界的关联。