Recent advances in single-cell RNA sequencing technologies enable deep insights into
cellular development, gene regulation, and phenotypic diversity by measuring gene …

Graph convolutional network (GCN) outputs powerful representation by considering the
structure information of the data to conduct representation learning, but its robustness is …

Finding meaningful representations and distances of hierarchical data is important in many
fields. This paper presents a new method for hierarchical data embedding and distance. Our …

One of the challenges in data analysis is to distinguish between different sources of
variability manifested in data. In this paper, we consider the case of multiple sensors …

Manifold learning aims to discover the low dimensional space where the input high
dimensional data are embedded by preserving the geometric structure. Unfortunately …

Recent studies show that multi-modal data fusion techniques combine information from
diverse sources for comprehensive diagnosis and prognosis of complex brain disorder …

Objective: Multi-modal brain functional connectivity (FC) data have shown great potential for
providing insights into individual variations in behavioral and cognitive traits. The joint …

We propose a novel algorithm for sleep dynamics visualization and automatic annotation by
applying diffusion geometry based sensor fusion algorithm to fuse spectral information from …

Canonical Correlation Analysis (CCA) models are powerful for studying the associations
between two sets of variables. The canonically correlated representations, termed\textit …

Selecting subsets of features that differentiate between two conditions is a key task in a
broad range of scientific domains. In many applications, the features of interest form clusters …