Projection techniques for nonlinear principal component analysis

Principal Components Analysis (PCA) is traditionally a linear technique for projecting multidimensional data onto lower dimensional subspaces with minimal loss of variance. However, there are several applications where the data lie in a lower dimensional subspace that is not linear; in these cases linear PCA is not the optimal method to recover this subspace and thus account for the largest proportion of variance in the data.Nonlinear PCA addresses the nonlinearity problem by relaxing the linear restrictions on standard PCA. We investigate both linear and nonlinear approaches to PCA both exclusively and in combination. In particular we introduce a combination of projection pursuit and nonlinear regression for nonlinear PCA. We compare the success of PCA techniques in variance recovery by applying linear, nonlinear and hybrid methods to some simulated and real data sets.We show that the best linear projection that captures the structure in the data (in the sense that the original data can be reconstructed from the projection) is not necessarily a (linear) principal component. We also show that the ability of certain nonlinear projections to capture data structure is affected by the choice of constraint in the eigendecomposition of a nonlinear transform of the data. Similar success in recovering data structure was observed for both linear and nonlinear projections.