Neural Conditional Energy Models for Multi-label Classification
How Jing, Shou-De Lin · 2014
Multi-label classification (MLC) is a type of structured output prediction problems where a given instance can be associated to more than one labels at a time. From the probabilistic point of view, a model predicts a set of labels y given an input vector v by learning a conditional distribution p(y|v). This paper presents a powerful model called a Neural Conditional Energy Model (NCEM) to solve MLC. The model can be viewed as a hybrid deterministic-stochastic network of which we use a deterministic neural network to transform the input data, before contributing to the energy landscape of v, y, and a single stochastic hidden layer h. Non-linear transformation given by the neural network makes our model more expressive and more capable of capturing complex relations between input and output, and using deterministic neurons facilitates exact inference. We present an efficient learning algorithm that is simple to implement. We conduct extensive experiments on 15 real-world datasets from wide variety of domains with various evaluation metrics to confirm that NCEM is significantly superior to current state-of-the-art models most of the time based on pair-wise t-test at 5% significance level. The MATLAB source code to replicate our experiments are available at https://github.com/Kublai-Jing/NCEM.