Greedy algorithms are widely used for problems in machine learning such as feature
selection and set function optimization. Unfortunately, for large datasets, the running time of …

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

In this brief, we propose the mixtures of probabilistic principal component analyzers with
latent bases having a common structure for modeling and monitoring multimodal processes …

The problem of selecting a small-size representative summary of a large dataset is a
cornerstone of machine learning, optimization and data science. Motivated by applications …

Variational inference is a popular technique to approximate a possibly intractable Bayesian
posterior with a more tractable one. Recently, boosting variational inference has been …

This paper presents new algorithms to solve the feature-sparsity constrained PCA problem
(FSPCA), which performs feature selection and PCA simultaneously. Existing optimization …

Sparse versions of principal component analysis (PCA) have imposed themselves as
simple, yet powerful ways of selecting relevant features of high-dimensional data in an …

The principal subspace estimation is directly connected to dimension reduction and is
important when there is more than one principal component of interest. In this article, we …

Collecting multiple modalities of neuroimaging data on the same subject is increasingly
becoming the norm in clinical practice and research. Fusing multiple modalities to find …

Abstract Sparse Principal Component Analysis (SPCA) and Sparse Linear Regression
(SLR) have a wide range of applications and have attracted a tremendous amount of …