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Jul, 2011
变分高斯过程动态系统
Variational Gaussian Process Dynamical Systems
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Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
TL;DR
本文介绍了一种非线性概率变分方法-变分高斯过程动态系统来处理高维时间序列数据中的非线性降维问题，同时在潜空间中学习动态先验，并允许自动确定潜在空间的维数，该方法在人体运动捕捉数据集和一系列高分辨率视频序列上进行了演示。
Abstract
high dimensional time series
are endemic in applications of
machine learning
such as robotics (sensor data), computational biology (gene expression data), vision (video sequences) and graphics (motion capture dat
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