This article presents a review of computational methods for connectivity inference from
neural activity data derived from multi-electrode recordings or fluorescence imaging. We first …

(57) ABSTRACT A method of modelling data, comprising: training an objec tive function of a
linear classifier, based on a set of labeled data, to derive a set of classifier weights; defining …

Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative
features for multi-class classification. A vast majority of existing LDA algorithms are prone to …

Feature selection (FS) aims to remove the irrelevant and redundant features to improve the
classification accuracy of the algorithm, which is regarded as an NP-hard problem. Recently …

In this paper, we propose a novel semisupervised feature selection framework by mining
correlations among multiple tasks and apply it to different multimedia applications. Instead of …

Both subspace learning methods and feature selection methods are often used for removing
irrelative features from high-dimensional data. Studies have shown that feature selection …

Feature selection for multiple types of data has been widely applied in mild cognitive
impairment (MCI) and Alzheimer's disease (AD) classification research. Combining multi …

Unsupervised feature selection has shown significant potential in distance-based clustering
tasks. This paper proposes a novel triplet induced method. Firstly, a triplet-based loss …

Principal component analysis (PCA) has been widely applied to dimensionality reduction
and data pre-processing for different applications in engineering, biology, social science …

Depression is a highly prevalent mental health problem and is a co-morbidity of other
mental, physical, and behavioural disorders. The internet allows individuals who are …