EM expectation maximisation

FA factor analysis

GTM generative topographic mapping

ICA independent component analysis

IFA independent factor analysis

MAP maximum a posteriori

MDL minimum description length

ML maximum likelihood

MLP multi-layer perceptron

PCA principal component analysis

RBF radial basis function

SOM self-organising map

ST signal transformation

**artificial neural network:**- A model which consists of simple
building-blocks. The development of such models has been inspired
by neurobiological findings. The building-blocks are termed neurons
in analogy to biological brain.
**auto-associative learning:**- Representation of the observations
is learned by finding a mapping from observations to themselves
through an ``information bottleneck'' which forces the model to
produce a compact coding of the observations. The
**recognition model**and**generative model**are learned simultaneously. **Bayesian probability theory:**- In Bayesian probability theory,
probability is a measure of subjective belief as opposed to
frequentist statistics where probability is interpreted as the
relative frequency of occurrences in an infinite sequence of
trials.
**factor:**- In
**generative models**, the regularities in the observations are assumed to have been caused by underlying factors, also termed hidden causes, latent variables or sources. **factor analysis:**- A technique for finding a
**generative model**which can represent some of the statistical structure of the observations. Usually refers to linear factor analysis where the generative model is linear. **feature:**- Feature describes a relevant aspect of observations.
The term is often used in connection with
**recognition models**. Bears resemblance to the term**factor**which is more common in connection to**generative models**. **ensemble learning:**- A technique for approximating the exact
application of
**Bayesian probability theory**. **generative model:**- A model which explicitly states how the
observations are assumed to have been generated. See
**recognition model**. **graphical model:**- A graphical representation of the causal
structure of a probabilistic model. Variables are denoted by
circles and arrows are used for representing the conditional
dependences.
**hidden cause:**- See
**factor**. **latent variable:**- See
**factor**. **posterior probability:**- Expresses the beliefs after making an
observation. Sometimes referred to as the posterior.
**prior probability:**- Expresses the beliefs before making an
observation. Sometimes referred to as the prior.
**probability density:**- Any single value of a continuous valued
variable usually has zero probability and only a finite range of
values has a nonzero probability. Probability of a continuous
variable can be characterised by probability density which is
defined to be the probability of a range divided by the size of the
range.
**probability mass:**- In analogy to physical mass and density,
ordinary probability can be called probability mass in order to
distinguish it from
**probability density**. **recognition model:**- A model which states how
**features**can be obtained from the observations. See**generative model**. **signal transformation approach:**- Finds a
**recognition model**by optimising a given criterion over the resulting features. **source:**- See
**factor**. **supervised learning:**- Aims at building a model which can mimic
the responses of a ``teacher'' who provides two sets of
observations: inputs and the corresponding desired outputs. See
**unsupervised learning**. **unsupervise learning:**- The goal in unsupervised learning is to
find an internal representation of the statistical structure of the
observations. See
**supervised learning**. **volume:**- In analogy to physical mass, density and volume, the
size of range of continuous valued variables can be called volume.
See
**probability density**.