4 citations · 7 across the 5 of their papers we have counts for
5 papers
Explainable AI Using Inherently Interpretable Components for Wearable-based Health Monitoring
Maurice Kuschel, Solveig Vieluf, Claus Reinsberger +2
The use of wearables in medicine and wellness, enabled by AI-based models, offers tremendous potential for real-time monitoring and interpretable event detection. Explainable AI (X…
Identifying the Complete Correlation Structure in Large-Scale High-Dimensional Data Sets with Local False Discovery Rates
Martin Gölz, Tanuj Hasija, Michael Muma +1
The identification of the dependent components in multiple data sets is a fundamental problem in many practical applications. The challenge in these applications is that often the…
Determining the Dimension and Structure of the Subspace Correlated Across Multiple Data Sets
Tanuj Hasija, Christian Lameiro, Timothy Marrinan +1
Detecting the components common or correlated across multiple data sets is challenging due to a large number of possible correlation structures among the components. Even more chal…
Determining the Dimension of the Improper Signal Subspace in Complex-Valued Data
Tanuj Hasija, Christian Lameiro, Peter J. Schreier
A complex-valued signal is improper if it is correlated with its complex conjugate. The dimension of the improper signal subspace, i.e., the number of improper components in a comp…
Canonical correlation analysis of high-dimensional data with very small sample support
Yang Song, Peter J. Schreier, David Ramirez +1
This paper is concerned with the analysis of correlation between two high-dimensional data sets when there are only few correlated signal components but the number of samples is ve…