论文标题
凸代表学学习半inner-inner产品空间中的普遍不变性的学习
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
论文作者
论文摘要
不变性（从一般意义上定义）一直是表示学习的最有效先验之一。参数模型的直接分解仅对于少量不向导是可行的，而正则化方法尽管有改善的一般性，但导致了非convex优化。在这项工作中，我们为各种可以将其建模为半道义建模的广义不变的凸表示学习算法。在半分子产品空间中引入了新型的欧几里得嵌入者，并建立了近似边界。如我们的实验中所确认的，这允许不变的表示能够有效地学习，并进行了准确的预测。
Invariance (defined in a general sense) has been one of the most effective priors for representation learning. Direct factorization of parametric models is feasible only for a small range of invariances, while regularization approaches, despite improved generality, lead to nonconvex optimization. In this work, we develop a convex representation learning algorithm for a variety of generalized invariances that can be modeled as semi-norms. Novel Euclidean embeddings are introduced for kernel representers in a semi-inner-product space, and approximation bounds are established. This allows invariant representations to be learned efficiently and effectively as confirmed in our experiments, along with accurate predictions.
