High-dimensional data is ubiquitous in scientific research and industrial production fields. It
brings a lot of information to people, at the same time, because of its sparse and …

Abstract Nowadays, Semi-Supervised Learning lies at the core of the Machine Learning field
trying to effectively exploit unlabeled data as much as possible, together with a small amount …

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 …

Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive
decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be …

To uncover an appropriate latent subspace for data representation, in this paper we propose
a novel Robust Structured Subspace Learning (RSSL) algorithm by integrating image …

Dimensionality reduction methods (DRs) have commonly been used as a principled way to
understand the high-dimensional data such as face images. In this paper, we propose a new …

Continuous Integration (CI) has become a best practice of modern software development.
Yet, at present, we have a shortfall of insight into the testing practices that are common in CI …

We propose a unified manifold learning framework for semi-supervised and unsupervised
dimension reduction by employing a simple but effective linear regression function to map …

This paper introduces a new framework for image classification using local visual
descriptors. The pipeline first performs a nonlinear feature transformation on descriptors …