逆特征学习：基于错误的表示学习的特征学习
Inverse Feature Learning: Feature learning based on Representation Learning of Error
摘要：
本文提出了逆特征学习作为一种新的监督特征学习技术，学习一组高层次的功能分类的基础上的错误表示方法。该方法的主要贡献是学习表示的错误作为高级功能，而目前的表示学习方法解释错误的损失函数，这是作为一个函数之间的差异获得的真实标签和预测的。这种学习方法的一个优点是每个类的学习特征独立于其他类的学习特征;因此，这种方法可以同时学习，这意味着它可以学习新的类而无需重新训练。错误表示学习还可以通过向原始数据集添加一组有影响力的特征来帮助泛化并减少过度拟合的机会，这些特征通过错误生成和分析过程捕获每个实例与不同类之间的关系。这种方法在数据集中特别有效，其中每个类的实例具有不同的特征表示或具有不平衡类的实例。实验结果表明，该方法的结果在显着更好的性能相比，国家的最先进的几个流行的数据集的分类技术。我们希望本文能够为错误表示学习的研究在不同的特征学习领域的应用开辟一条新的途径。
英文摘要：
This paper proposes inverse feature learning as a novel supervised feature learning technique that learns a set of high-level features for classification based on an error representation approach. The key contribution of this method is to learn the representation of error as high-level features, while current representation learning methods interpret error by loss functions which are obtained as a function of differences between the true labels and the predicted ones. One advantage of such learning method is that the learned features for each class are independent of learned features for other classes; therefore, this method can learn simultaneously meaning that it can learn new classes without retraining. Error representation learning can also help with generalization and reduce the chance of over-fitting by adding a set of impactful features to the original data set which capture the relationships between each instance and different classes through an error generation and analysis process. This method can be particularly effective in data sets, where the instances of each class have diverse feature representations or the ones with imbalanced classes. The experimental results show that the proposed method results in significantly better performance compared to the state-of-the-art classification techniques for several popular data sets. We hope this paper can open a new path to utilize the proposed perspective of error representation learning in different feature learning domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
