This work further develops on the Lawrence et al. papers [1] and [2]. We first replicate the algorithm and evaluate it on the same datasets as the original author. In addition, we apply it to new data and provide a more extensive assessment of its performance against similar methods such as PCA and Kernel PCA.

The original formulation of the Gaussian Process Latent Variable Model (GPLVM) can be regarded as an extension of the Probabilistic Principal Component Analysis (PPCA) introduced by Tipping and Bishop [3] some years earlier. Thus, the GPLVM consists on the introduction of the kernel trick intuition in this probabilistic reformulation of PCA, that leads to a decomposition of the data represented in higher dimensional spaces.

Projection of 48-dimensional features into a 2 dimensional latent space of a subset of mice gene dataset with real classes (left) and their corresponding GMM clustering (right). Notice how clustering in GPLVM projection greatly outperforms the one provided by PCA:

Given a set of unordered video frames, reordering can be achieved by their projection into a 1-dimensional latent space. Once again, GPLVM noticebly outperforms results yielded by simple PCA.

Input sequence (read from top to bottom, left to right):

PCA reconstruction:

GPLVM reconstruction:

Further experiments and quantitative analysis of the results can be found here

[1] Neil Lawrence. Probabilistic non-linear principal component analysis with gaussian process latent variablemodels. Journal of machine learning research, 6(Nov):1783–1816, 2005

[2] Neil D Lawrence. Gaussian process latent variable models for visualisation of high dimensional data. In Advances in neural information processing systems, pages 329–336, 2004.

[3] Michael E Tipping and Christopher M Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(3):611–622, 1999.

Note: This work was done as part of KTH Advanced Machine Learning Final Project DD2434 2019