论文标题
取决于输出的高斯过程状态空间模型
Output-Dependent Gaussian Process State-Space Model
论文作者
论文摘要
高斯工艺状态空间模型（GPSSM）是一个完全概率的状态空间模型，在过去十年中引起了很多关注。但是，假定现有GPSSM中过渡函数的输出被认为是独立的，这意味着GPSSM不能利用不同输出之间的电感偏差并失去某些模型能力。为了解决这个问题，本文通过利用众所周知的，简单但实用的线性线性模型（LMC）框架来代表输出依赖性，提出了一个与输出有关的GPSSM。为了共同学习依赖输出的GPSSM并推断潜在状态，我们提出了一种基于稀疏GP的学习方法，只会轻轻地提高计算复杂性。合成数据集和真实数据集的实验证明了在学习和推理性能方面取决于输出依赖性GPSSM的优势。
Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade. However, the outputs of the transition function in the existing GPSSMs are assumed to be independent, meaning that the GPSSMs cannot exploit the inductive biases between different outputs and lose certain model capacities. To address this issue, this paper proposes an output-dependent and more realistic GPSSM by utilizing the well-known, simple yet practical linear model of coregionalization (LMC) framework to represent the output dependency. To jointly learn the output-dependent GPSSM and infer the latent states, we propose a variational sparse GP-based learning method that only gently increases the computational complexity. Experiments on both synthetic and real datasets demonstrate the superiority of the output-dependent GPSSM in terms of learning and inference performance.
