The behaviour of time series data from financial markets is influenced by a rich mixture of
quantitative information from the dynamics of the system, captured in its past behaviour, and …

Background The classification of cancer subtypes is of great importance to cancer disease
diagnosis and therapy. Many supervised learning approaches have been applied to cancer …

Automated diagnosis systems aim to reduce the cost of diagnosis while maintaining the
same efficiency. Many methods have been used for breast cancer subtype classification …

Abstract The Southern Region has reported a large number of contagious pandemic
outbreaks. These epidemics brought threats to human health and resulted in serious …

While non-negative blind source separation (nBSS) has found many successful applications
in science and engineering, model order selection, determining the number of sources …

To identify biomarkers for glioma growth, invasion and progression, we used a candidate
gene approach in mouse models with two complementary brain tumour phenotypes …

Mixed Membership Models (MMMs) are a popular family of latent structure models for
complex multivariate data. Instead of forcing each subject to belong to a single cluster …

Matrix decomposition is one of the fundamental tools to discover knowledge from big data
generated by modern applications. However, it is still inefficient or infeasible to process very …

For many classic structured prediction problems, probability distributions over the dependent
variables can be efficiently computed using widely-known algorithms and data structures …

Matrix decomposition is a popular and fundamental approach in machine learning. The
classical matrix decomposition methods with Frobenius norm loss is only optimal for …