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中文摘要：
高斯过程隐变量模型是近年来新兴的无监督降维方法，它可以找到高维数据的低维流形结构．但是由于高斯过程隐变量模型是无监督的概率降维方法，所以当数据集中的样本有类别标记信息时，高斯过程隐变量模型不能利用这些监督信息，实现分类的任务．为了使高斯过程隐变量模型可以处理分类任务，文中提出了一种监督的高斯过程隐变量模型分类模型．通过最大化后验似然的方法确定观测数据在隐空间的坐标，同时可以完成分类任务．实验结果证明了该模型可以有效地用于分类．
英文摘要：
Gaussian process latent variable model is a new probabilistic approach for dimensionality reduction. It can obtain a low-dimensional manifold of a data set in an entirely unsupervised way. However, when there is some supervised information in the data set, Gaussian process latent var- iable model cannot use this information for supervised tasks, e. g. , classification and regression learning. For this purpose, a supervised Gaussian process latent variable model for classification is developed. The maximum-a-posterior algorithm is employed to estimate all latent variables position. Compared with the traditional Gaussian process latent variable model, the supervised version of this model shows more advantages in experiments.
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Similarity Constraints-Based Structured Output Regression Machine: An Approach to Image Super-Resolu
Multi-Kernel Implicit Curve Evolution for Selected Texture Regions Segmentation in VHR Satellite Ima
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