Online Metric Learning for Multi-Label Classification

By Xiuwen Gong, Dong Yuan, Wei Bao. 2020

15 citations recorded by the source corpus.

Existing research into online multi-label classification, such as online sequential multi-label extreme learning machine (OSML-ELM) and stochastic gradient descent (SGD), has achieved promising performance. However, these works lack an analysis of loss function and do not consider label dependency. Accordingly, to fill the current research gap, we propose a novel online metric learning paradigm for multi-label classification. More specifically, we first project instances and labels into a lower dimension for comparison, then leverage the large margin principle to learn a metric with an efficient optimization algorithm. Moreover, we provide theoretical analysis on the upper bound of the cumulative loss for our method. Comprehensive experiments on a number of benchmark multi-label datasets validate our theoretical approach and illustrate that our proposed online metric learning (OML) algorithm outperforms state-of-the-art methods.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Tong Lu
2. Wenhai Wang
3. Guangchen Shi
4. J. Liu
5. Yirui Wu
6. Shaohua Wan
7. Jianwei Yang
8. Meng-Hao Guo

Citations in the corpus

• Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation (2022, methodology): “Metric learning based methods [12, 41, 43] firstly learn a projection function that projects the inputs to an embedding space, and then define a certain distance metric that measures the distance between any two embeddings.”