Table 1.

Glossary of terms.

Term	Definition
Variance	$\frac{{\sum }_{i=1}^n{(x_i-\overline{x})}^2}{n-1}$; sample variance is usually interpreted as the average squared deviation from the mean
Covariance	$\mathit{cov}(x,y)={\sum }_{i=1}^N\frac{(x_i-\overline{x})(y_i-\overline{y})}{N}$; a measure used to quantify the relationship between two random variables
Correlation	$\mathit{cor}(x,y)=\frac{\mathit{cov}(x,y)}{{\sigma }_x{\sigma }_y}$; where σ denotes standard deviation; unlike covariance, correlation is a unitless measure of the relationship between two random variables
Orthogonal transformation	A linear transformation is called orthogonal if it preserves the length of the vectors; $\parallel T(\overrightarrow{x})\parallel =\parallel \overrightarrow{x}\parallel$. In PCA, the solution forces each component to be orthogonal to the previous, i.e., independent.
Matrix factorization	Decomposition of a matrix into the product of two or more lower-dimension rectangular matrices
Latent variable	An unobserved (“hidden”) variable that is inferred through observed variables
Bayesian Information Criterion (BIC)	Determines model fit by considering the likelihood function of a model, number of data points, and number of free parameters to be estimated. It is a criterion used for model selection; the model with lowest BIC is preferred.
Clique	In graph theory, a complete subgraph is called a clique. A graph is complete when every pair of distinct vertices—a corner or a point where lines meet—is connected by a unique edge, i.e., every vertex has an edge to every other vertex.
Paraclique	It consists of a clique and all vertices with at least some proportion of edges to the clique. It is considered a relaxation of clique.