标题： 逆特征学习：基于误差表示学习的特征学习 显示英文标题

标题： Inverse Feature Learning: Feature learning based on Representation Learning of Error

摘要： 本文提出逆特征学习作为一种新颖的监督特征学习技术，该技术基于误差表示方法，学习一组用于分类的高级特征。 该方法的关键贡献是将误差的表示作为高级特征进行学习，而当前的表示学习方法通过损失函数来解释误差，这些损失函数是真值标签与预测结果之间差异的函数。 这种学习方法的一个优势是，每个类别的学习特征与其他类别的学习特征相互独立；因此，该方法可以同时学习，即可以在不重新训练的情况下学习新类别。 误差表示学习还可以通过添加一组有影响力的特征到原始数据集中，帮助提高泛化能力并减少过拟合的可能性，这些特征通过误差生成和分析过程捕捉每个实例与不同类别之间的关系。 该方法在实例具有多样特征表示或类别不平衡的数据集中尤其有效。 实验结果表明，与几种流行数据集上的最新分类技术相比，所提出的方法取得了显著更好的性能。 我们希望本文能为在不同特征学习领域利用所提出的误差表示学习视角开辟一条新的路径。 显示英文摘要

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.