Many annotation projects have shown that the quality of manual annotations often is not as good as would be desirable for reliable data analysis. Identifying the main sources responsible for poor annotation quality must thus be a major concern. Generalizability theory is a valuable tool for this purpose, because it allows for the differentiation and detailed analysis of factors that influence a...

Generalizability in a multi-subject fMRI study is investigated. The analysis is based on principal and independent component representations. Subsequent supervised learning and classification is carried out by canonical variates analysis and clustering methods. The generalization error is estimated by cross-validation, forming the so-called learning curves. The fMRI case story is a motor-contro...

We investigate the generalizability of deep learning based on the sensitivity to input perturbation. We hypothesize that the high sensitivity to the perturbation of data degrades the performance on it. To reduce the sensitivity to perturbation, we propose a simple and effective regularization method, referred to as spectral norm regularization, which penalizes the high spectral norm of weight m...

Small sample high-dimensional principal component analysis (PCA) suffers from variance inflation and lack of generalizability. It has earlier been pointed out that a simple leave-one-out variance renormalization scheme can cure the problem. In this paper we generalize the cure in two directions: First, we propose a computationally less intensive approximate leave-one-out estimator, secondly, we...