Data reduction plays an important role in machine learning and pattern recognition with a high-dimensional data. In real-world applications data usually exists with hybrid formats...
The problem of nonlinear dimensionality reduction is considered. We focus on problems where prior information is available, namely, semi-supervised dimensionality reduction. It is...
Xin Yang, Haoying Fu, Hongyuan Zha, Jesse L. Barlo...
Linear Discriminant Analysis (LDA) is one of the most popular approaches for feature extraction and dimension reduction to overcome the curse of the dimensionality of the high-dime...
A new model for the One-dimensional Cutting Stock problem using Genetic Algorithms (GA) is developed to optimize construction steel bars waste. One-dimensional construction stocks...
Dimensionality reduction via Random Projections has attracted considerable attention in recent years. The approach has interesting theoretical underpinnings and offers computation...