Training Single-Layer Morphological Perceptron Using Convex-Concave
  Programming
Iara Cunha Marcos Eduardo Valle
This paper concerns the training of a single-layer morphological perceptron
using disciplined convex-concave programming (DCCP). We introduce an algorithm
referred to as K-DDCCP, which combines the existing single-layer morphological
perceptron (SLMP) model proposed by Ritter and Urcid with the weighted
disciplined convex-concave programming (WDCCP) algorithm by Charisopoulos and
Maragos. The proposed training algorithm leverages the disciplined
convex-concave procedure (DCCP) and formulates a non-convex optimization
problem for binary classification. To tackle this problem, the constraints are
expressed as differences of convex functions, enabling the application of the
DCCP package. The experimental results confirm the effectiveness of the K-DDCCP
algorithm in solving binary classification problems. Overall, this work
contributes to the field of morphological neural networks by proposing an
algorithm that extends the capabilities of the SLMP model.