Constrainfit

Purpose

Finds A minimizing ||X-A*B'|| subject to constraints, given the small matrices (X ' B) and (B ' B)

Synopsis

[A]=constrainfit(XB,BtB,Aold);  % Unconstrained

Setting global constraints on A

opt = constrainfit('options');

opt.type='nonnegativity';

[A]=constrainfit(XB,BtB,Aold,opt); % Nonnegative

Setting constraints on just one column of A

opt = constrainfit('options');

opt.type='columnwise';

opt.columnconstraints={0;2;0}; % If three columns

[A]=constrainfit(XB,BtB,Aold,opt); % Second column unimodal

Description

CONSTRAINTFIT solves the least squares problem behind bilinear, trilinear and other multilinear models. Assuming a model X = A*B ' and assuming that X and B are known, the least squares estimate of A is obtained. Rather than using X and B this algorithm uses the cross product matrices (X ' B) and (B ' B) which are generally smaller and less memory-demanding especially in multi-way models.

CONSTRAINFIT can do a number of general types of regression problems such as nonnegativity-constrained regression, regression with column-orthogonality of A etc. These constraints are simply set in the option field 'type', e.g. option.type='nonnegativity'. Thus, for most problems, only the 'type' field needs to be set. CONSTRAINFIT will provide a least squares solution to most of these problems.

CONSTRAINFIT can also find A subject to different constraints on different columns. In this case, the update of A will be an improvement of the initially provided estimate Aold. As CONSTRAINFIT is used inside iterative algorithms, an improvement is sufficient to guarantee overall convergence.

Inputs

• XB = This is the matrix X ' B.
• BtB = This is the matrix B ' B.
• Aold = An initial estimate of A.

Optional Inputs

• options = provides definitions for which type of constraint to impose.

Outputs

• A = The improved estimate of A.

Options

options = a structure array with the following fields:

• type: [ {'unconstrained'} | 'nonnegativity' | 'unimodality' | 'orthogonality' | 'columnorthogonal' | 'equality' | 'exponential' | 'rightprod' | 'columnwise']

• columnconstraints: cell where element f defines constraints on column f (only applicable if options.type = 'columnwise'). For applicable column constraints see below.
• inequality : Defines a cutoff. If inequality is defined in columnwise, all elements of that column will be > options.inequality.scalar. Thus, when set to zero, nonnegativity is imposed.
• nonnegativity: defines which algorithm to use for imposing nonnegativity when options.type = 'nonnegativity'. If set to 0, the default NNLS algorithm is used. If set to 1, a faster columnwise update is used which only improves the current least squares fit, if set to 2, an ad hoc approach is used where A is estimated in a least squares sense and then negative numbers are set to zero. This will not provide a well-defined solution in terms of the least squares loss function. If set to 3, the NMF algorithm is used. This requires that all elements of the data array are nonnegative in order to work properly.
• smoothness: defines how much smoothness is imposed when smoothness is imposed as a columnconstraint. smoothness.alpha is a number between 0 (no smoothness) and 1 (full smoothness)
• fixed:
• advanced:
• equality:
• unimodality:
• functional:
• definitions: @optiondefs

Example

>>This is an example
% Make a noisy dataset
load aminoacids
x = X.data;
x = x+randn(size(x))*100;

% define parafac options
op=parafac('options');

% set constraints of columns in second mode
op.constraints{2}.type='columnwise';
% Define that first column is smooth, second and third unconstrained
op.constraints{2}.columnconstraints={4 0 0};
% Fit model
model = parafac(x,3,op);

Note how the first loading in the second mode is more smooth than the rest

if needed smoothness can be turned up (to one) and down (to zero) using
op.constraints{2}.smoothness.alpha=0.6

See Also

baselinew, deresolv