This paper surveys main principles of feature selection and their recent applications in big
data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and …

In complex pattern recognition tasks, objects are typically characterized by means of
multimodality attributes, including categorical, numerical, text, image, audio, and even …

With the rapid development of multimedia technology, massive unlabelled data with high
dimensionality need to be processed. As a means of dimensionality reduction, unsupervised …

Feature selection is an important approach for reducing the dimension of high-dimensional
data. In recent years, many feature selection algorithms have been proposed, but most of …

This study explored the feasibility of rapid detection of biogenic amines (BAs) in cooked beef
during the storage process using hyperspectral imaging technique combined with sparse …

Based on spectral graph theory, spectral clustering is an optimal graph partition problem. It
has been proven that the spectral clustering is equivalent to nonnegative matrix factorization …

Manifold is considered to be a low dimensional surface embedded in a high dimensional
vector space, and manifold learning is to find this surface based on data points sampled …

Spectral clustering based subspace clustering methods have emerged recently. When the
inputs are 2-dimensional (2D) data, most existing clustering methods convert such data to …

This paper presents a new flexible approach to predict the gender of the writers from their
handwriting samples. Handwriting features like slant, curvature, line separation, chain code …

In this paper, we propose a multi-kernel classifier learning algorithm to optimize a given
nonlinear and nonsmoonth multivariate classifier performance measure. Moreover, to solve …