Learning for Larger Datasets with the Gaussian Process Latent Variable Model
نویسنده
چکیده
In this paper we apply the latest techniques in sparse Gaussian process regression (GPR) to the Gaussian process latent variable model (GPLVM). We review three techniques and discuss how they may be implemented in the context of the GP-LVM. Each approach is then implemented on a well known benchmark data set and compared with earlier attempts to sparsify the model.
منابع مشابه
A Gaussian process latent variable model formulation of canonical correlation analysis
We investigate a nonparametric model with which to visualize the relationship between two datasets. We base our model on Gaussian Process Latent Variable Models (GPLVM)[1],[2], a probabilistically defined latent variable model which takes the alternative approach of marginalizing the parameters and optimizing the latent variables; we optimize a latent variable set for each dataset, which preser...
متن کاملVariational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression
We introduce a novel variational method that allows to approximately integrate out kernel hyperparameters, such as length-scales, in Gaussian process regression. This approach consists of a novel variant of the variational framework that has been recently developed for the Gaussian process latent variable model which additionally makes use of a standardised representation of the Gaussian proces...
متن کاملVariational Inference for Uncertainty on the Inputs of Gaussian Process Models
The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dimensionality reduction that has been widely applied. However, the current approach for training GP-LVMs is based on maximum likelihood, where the latent projection variables are maximized over rather than integrated out. In this paper we present a Bayesian method for training GP-LVMs by introducing...
متن کاملModeling Human Locomotion with Topologically Constrained Latent Variable Models
Learned, activity-specific motion models are useful for human pose and motion estimation. Nevertheless, while the use of activityspecific models simplifies monocular tracking, it leaves open the larger issues of how one learns models for multiple activities or stylistic variations, and how such models can be combined with natural transitions between activities. This paper extends the Gaussian p...
متن کاملGaussian Process Latent Variable Alignment Learning
We present a model that can automatically learn alignments between high-dimensional data in an unsupervised manner. Learning alignments is an ill-constrained problem as there are many different ways of defining a good alignment. Our proposed method casts alignment learning in a framework where both alignment and data are modelled simultaneously. We derive a probabilistic model built on non-para...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2007