Curse of dimensionality is known as big challenges in data mining, pattern recognition,
computer vison and machine learning in recent years. Feature selection and feature …

This survey aims at providing a state-of-the-art overview of feature selection and fusion
strategies, which select and combine multi-view features effectively to accomplish …

Feature selection, as a dimensionality reduction technique, aims to choosing a small subset
of the relevant features from the original features by removing irrelevant, redundant or noisy …

Feature selection has aroused considerable research interests during the last few decades.
Traditional learning-based feature selection methods separate embedding learning and …

In many data analysis tasks, one is often confronted with very high dimensional data.
Feature selection techniques are designed to find the relevant feature subset of the original …

Feature selection (FS) is an important component of many pattern recognition tasks. In these
tasks, one is often confronted with very high-dimensional data. FS algorithms are designed …

Decision trees have become one of the most powerful and popular approaches in
knowledge discovery and data mining; it is the science of exploring large and complex …

Offering a fundamental basis in kernel-based learning theory, this book covers both
statistical and algebraic principles. It provides over 30 major theorems for kernel-based …

Principal components analysis (PCA) is a classic method for the reduction of dimensionality
of data in the form of n observations (or cases) of a vector with p variables. Contemporary …

Feature selection aims to reduce dimensionality for building comprehensible learning
models with good generalization performance. Feature selection algorithms are largely …