论文标题
稀疏的无限随机特征潜在变量建模
Sparse Infinite Random Feature Latent Variable Modeling
论文作者
论文摘要
我们提出了一个非线性的贝叶斯非参数潜在变量模型，其中假定潜在空间是稀疏和无限的尺寸，先验使用印度自助餐工艺。后验，潜在空间中实例化尺寸的数量保证是有限的。将印度自助餐过程放在潜在变量上的目的是：1。）自动和概率地选择潜在维度的数量。 2.）在潜在空间中施加稀疏性，其中印度自助餐过程将选择哪些元素完全为零。我们提出的模型允许在自动选择潜在维度的数量的情况下进行稀疏的非线性潜在变量建模。使用随机傅立叶近似进行推理，我们可以轻松地通过马尔可夫链蒙特卡洛采样实现后推理。这种方法适合除高斯环境之外的许多观察模型。我们在各种合成，生物学和文本数据集上演示了我们方法的实用性，并表明我们可以获得与以前的潜在变量模型相比，可以获得卓越的测试集。
We propose a non-linear, Bayesian non-parametric latent variable model where the latent space is assumed to be sparse and infinite dimensional a priori using an Indian buffet process prior. A posteriori, the number of instantiated dimensions in the latent space is guaranteed to be finite. The purpose of placing the Indian buffet process on the latent variables is to: 1.) Automatically and probabilistically select the number of latent dimensions. 2.) Impose sparsity in the latent space, where the Indian buffet process will select which elements are exactly zero. Our proposed model allows for sparse, non-linear latent variable modeling where the number of latent dimensions is selected automatically. Inference is made tractable using the random Fourier approximation and we can easily implement posterior inference through Markov chain Monte Carlo sampling. This approach is amenable to many observation models beyond the Gaussian setting. We demonstrate the utility of our method on a variety of synthetic, biological and text datasets and show that we can obtain superior test set performance compared to previous latent variable models.
