Training Single-Layer Morphological Perceptron Using Convex-Concave Programming
This work contributes incrementally to morphological neural networks by extending the capabilities of an existing model for binary classification tasks.
The paper tackles training a single-layer morphological perceptron for binary classification by proposing the K-DDCCP algorithm, which combines existing models with disciplined convex-concave programming, and experimental results confirm its effectiveness.
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.