Modelling sequential data is important in many areas of science and engineering. Hidden
Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they …

The notion of defining a cluster as a component in a mixture model was put forth by
Tiedeman in 1955; since then, the use of mixture models for clustering has grown into an …

Summarising a high dimensional data set with a low dimensional embedding is a standard
approach for exploring its structure. In this paper we provide an overview of some existing …

In this paper, we develop a theory of matrix completion for the extreme case of noisy 1-bit
observations. Instead of observing a subset of the real-valued entries of a matrix M, we …

We consider a logistic regression model with a Gaussian prior distribution over the
parameters. We show that an accurate variational transformation can be used to obtain a …

" A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best
introductory book for ML currently available. It combines rigor and precision with …

Principal component analysis (PCA) is a commonly applied technique for dimensionality
reduction. PCA implicitly minimizes a squared loss function, which may be inappropriate for …

Principal component analysis (PCA) for binary data, known as logistic PCA, has become a
popular alternative to dimensionality reduction of binary data. It is motivated as an extension …

We investigate a generalized linear model for dimensionality reduction of binary data. The
model is related to principal component analysis (PCA) in the same way that logistic …

The maximum-likelihood estimates of a principal component analysis on the logit or probit
scale are computed using majorization algorithms that iterate a sequence of weighted or …