Principal components analysis (PCA) has been widely used as a statistical tool for the
dimension reduction of multivariate data in various application areas and extensively …

With the scale of data growing every day, reducing the dimensionality (aka sketching) of
high-dimensional vectors has emerged as a task of increasing importance. Relevant issues …

We address the problem of interactive feature construction and denoising of binary data. To
this end, we develop a variational ICA method, employing a multivariate Bernoulli likelihood …

Latent variable models are ubiquitous in machine learning and statistics and are core
components of many of the most widely-used probabilistic models, including mixture models …

A directed generative model for binary data using a small number of hidden continuous units
is investigated. A clipping nonlinear (cid: 173) ity distinguishes the model from conventional …

This paper deals with the problem of probability density estimation with the goal of finding a
good probabilistic representation of the data. One of the most popular density estimation …

In 1949, when Eric Blair (aka George Orwell) published the “1984” novel, the idea of
omnipresent surveillance systems was considered a dangerous utopia. When 50 years later …

Factor analysis and related models for probabilistic matrix factorisation are of central
importance to the unsupervised analysis of data, with a colourful history more than a century …

We consider a visual scene analysis scenario where objects (eg people, cars) pass through
the viewing field of a static camera and need to be detected and segmented from the …

New Zealand police has long been suspected of systematic bias against the indigenous
Māori. One resource available to investigate this possibility is the annual counts of police …