Out-of-Sample Embedding with Proximity Data: Projection Versus Restricted Reconstruction
本文综述了利用邻近数据（相似性或相异性）将新点添加到已有向量图中的样本外嵌入方法，将现有核方法归纳为投影法和受限重建法两种策略，并比较了各自的适用场景。
The problem of using proximity (similarity or dissimilarity) data for the purpose of “adding a point to a vector diagram” was first studied by J. C. Gower in 1968. Since then, a number of methods—mostly kernel methods—have been proposed for solving what has come to be called the problem of out-of-sample embedding. We survey the various kernel methods that we have encountered and show that each can be derived from one or the other of two competing strategies: projection or restricted reconstruction. Projection can be analogized to a well-known formula for adding a point to a principal component analysis. Restricted reconstruction poses a different challenge: how to best approximate redoing the entire multivariate analysis while holding fixed the vector diagram that was previously obtained. This strategy results in a nonlinear optimization problem that can be simplified to a unidimensional search. Various circumstances may warrant either projection or restricted reconstruction.