Constrained large Margin Local Projection algorithms and extensions for multimodal dimensionality reduction

A Constrained large Margin Local Projection (CMLP) technique for multimodal dimensionality reduction is proposed. We elaborate the criterion of CMLP from a pairwise constrained marginal perspective. Four effective CMLP solution schemes are presented and the corresponding comparative analyses are given. An equivalent weighted least squares formulation for CMLP is also detailed. CMLP is originated from the criterion of Locality Preserving Projections (LPP), but CMLP offers a number of attractive advantages over LPP. To keep the intrinsic proximity relations of inter-class and intra-class similarity pairs, the localized pairwise Cannot-Link and Must-Link constraints are applied to specify the types of those neighboring pairs. By utilizing the CMLP criterion, margins between inter- and intra-class clusters are significantly enlarged. As a result, multimodal distributions are effectively preserved. To further optimize the CMLP criterion, one feasible improvement strategy is described. With kernel methods, we present the kernelized extensions of our approaches. Mathematical comparisons and analyses between this work and the related works are also detailed. Extensive simulations including multivariate manifold visualization and classification on the benchmark UCL, ORL, YALE, UMIST, MIT CBCL and USPS datasets are conducted to verify the efficiency of our techniques. The presented results reveal that our methods are highly competitive with and even outperform some widely used state-of-the-art algorithms.

► A Constrained large Margin Local Projection (CMLP) technique for multimodal dimensionality reduction is proposed.
► Four effective CMLP solution schemes are presented and the corresponding comparative analyses are given.
► One feasible improvement strategy is presented to optimize the CMLP criterion.
► A weighted least squares view of CMLP is elaborated.
► The theoretical comparisons between this work and the related works show our presented CMLP is more general.