# Package-level declarations

## Functions

Generalized Hebbian Algorithm. GHA is a linear feed-forward neural network model for unsupervised learning with applications primarily in principal components analysis. It is single-layer process -- that is, a synaptic weight changes only depending on the response of the inputs and outputs of that layer.

Generalized Hebbian Algorithm with random initial projection matrix.

Kernel principal component analysis. Kernel PCA is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with a non-linear mapping.

Principal component analysis. PCA is an orthogonal linear transformation that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. PCA is theoretically the optimum transform for given data in least square terms. PCA can be thought of as revealing the internal structure of the data in a way which best explains the variance in the data. If a multivariate dataset is visualized as a set of coordinates in a high-dimensional data space, PCA supplies the user with a lower-dimensional picture when viewed from its (in some sense) most informative viewpoint.

Probabilistic principal component analysis. PPCA is a simplified factor analysis that employs a latent variable model with linear relationship: