On Kernel Parameter Selection in Hilbert-Schmidt Independence Criterion

On Kernel Parameter Selection in Hilbert-Schmidt Independence Criterion

The Hilbert-Schmidt independence criterion (HSIC) is a kernel-based statistical independence measure that can be computed very efficiently. However, it requires us to determine the kernel parameters heuristically because no objective model selection method is available. Least-squares mutual information (LSMI) is another statistical independence measure that is based on direct density-ratio esti...

Semi-supervised Dictionary Learning Based on Hilbert-Schmidt Independence Criterion

In this paper, a novel semi-supervised dictionary learning and sparse representation (SS-DLSR) is proposed. The proposed method benefits from the supervisory information by learning the dictionary in a space where the dependency between the data and class labels is maximized. This maximization is performed using Hilbert-Schmidt independence criterion (HSIC). On the other hand, the global distri...

Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment: Supplementary Materials

Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment

Canonical correlation analysis (CCA) is a well established technique for identifying linear relationships among two variable sets. Kernel CCA (KCCA) is the most notable nonlinear extension but it lacks interpretability and robustness against irrelevant features. The aim of this article is to introduce two nonlinear CCA extensions that rely on the recently proposed Hilbert-Schmidt independence c...

Geometric Analysis of Hilbert-Schmidt Independence Criterion Based ICA Contrast Function

Since the success of Independent Component Analysis (ICA) for solving the Blind Source Separation (BSS) problem [1, 2], ICA has received considerable attention in numerous areas, such as signal processing, statistical modeling, and unsupervised learning. The performance of ICA algorithms depends significantly on the choice of the contrast function measuring statistical independence of signals a...