高斯过程隐变量模型是最近提出的比较流行的无监督降维方法。
Gaussian process latent variable model (GPLVM) is a popular manifold method recently proposed for dimensional reduction.
本文介绍隐变量上的箭头指向问题、模型的等价及检验的功效。
We introduce the direction of arrows on the latent variable, equivalent model and power of test.
首先以分阶段的诺西肽发酵过程非结构模型为基础，根据隐函数存在定理进行辅助变量的合理选择；
Firstly, based on the staged unstructured model of Nosiheptide fermentation process, secondary variables were selected according to the implicit function existence theorem.
把模型的变量转化为二进制变量，并用隐枚举法求出最优解。
All variables were translated into binary variables, and implicit enumeration method was developed to solve the model.
把模型的变量转化为二进制变量，并用隐枚举法求出最优解。
All variables were translated into binary variables, and implicit enumeration method was developed to solve the model.
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