Real data often show that from appearance within-class similarity is relatively low and
between-class similarity is relatively high, which could increase the difficulty of classification …

Recent research has witnessed the fostered application of machine learning approaches in
analyzing the single nucleotide polymorphisms (SNP) data, which has been proved to be …

Quality of Service (QoS) value is usually unknown in service recommendation practice.
There are some matrix factorization approaches for predicting the unknown value with a …

Many problems in computer vision and pattern recognition can be posed as learning low-
dimensional subspace structures from high-dimensional data. Subspace clustering …

Composing a realistic picture according to the mind is tough work for most people. It is not
only a complex operation but also a creation process from nonexistence to existence …

As a variant of Finite mixture model (FMM), finite Inverted Dirichlet mixture model (IDMM)
can not avoid the conventional challenges, such as how to select the appropriate number of …

Semi-supervised classification receives increasing interests because it can predict class
labels based on both limited labeled and sufficient unlabeled data. In this letter, we propose …

This study emphasizes the classification of different cardiac diseases through bio-inspired
classifiers with and without hyperparameters selection. Principal Component Analysis …

Low-rank matrix factorizations such as Principal Component Analysis (PCA), Singular Value
Decomposition (SVD) and Non-negative Matrix Factorization (NMF) are a large class of …

In supervised learning, the gap between the truth label and the model output is always
portrayed by an error function, and a fixed error function corresponds to a specific noise …