  In this paper, we propose an alternative method to the disjoint principal
component analysis. The method consists of a principal component analysis with
constraints, which allows us to determine disjoint components that are linear
combinations of disjoint subsets of the original variables. The proposed method
is named constrained binary optimization by particle swarm disjoint principal
component analysis, since it is based on the particle swarm optimization. The
method uses stochastic optimization to find solutions in cases of high
computational complexity. The algorithm associated with the method starts
generating randomly a particle population which iteratively evolves until
attaining a global optimum which is function of the disjoint components.
Numerical results are provided to confirm the quality of the solutions attained
by the proposed method. Illustrative examples with real data are conducted to
show the potential applications of the method.

Neural and Evolutionary Computing;Machine Learning;Computation