# Coordinate-based Dimension Reduction¶

The goal of a coordinate based dimension reduction is to identify subsets of variables that are sufficient to explain the behavor of the function; this process is sometimes known as variable screening.

Although in many ways this is a simpler process than subspace based dimension reduction, from the point of view of the code, a coordinate-based dimension reduction is a special case of a subspace-based dimension reduction where subspaces are built from columns of the identity matrix.

class psdr.CoordinateBasedDimensionReduction[source]

Abstract base class for dimension reduction strategies that select variables

U

A matrix defining the ‘important’ directions

Returns: Matrix with orthonormal columns defining the important directions in decreasing order of precidence. np.ndarray (m, n)
score

The score associated with each parameter

## Diagonal Lipschitz Matrix¶

class psdr.DiagonalLipschitzMatrix(L=None, **kwargs)[source]

Constructs a diagonal Lipschitz matrix

Much like the standard Lipschitz matrix class psdr.LipschitzMatrix(), this class computes a Lipschitz matrix except with the constraint the matrix is diagonal.