36 citations · 76 across the 5 of their papers we have counts for
4 papers · 1 filter
All Sparse PCA Models Are Wrong, But Some Are Useful. Part I: Computation of Scores, Residuals and Explained Variance
J. Camacho, A. K. Smilde, E. Saccenti +1
Sparse Principal Component Analysis (sPCA) is a popular matrix factorization approach based on Principal Component Analysis (PCA) that combines variance maximization and sparsity w…
Heterofusion: Fusing genomics data of different measurement scales
Age K. Smilde, Yipeng Song, Johan A. Westerhuis +3
In systems biology, it is becoming increasingly common to measure biochemical entities at different levels of the same biological system. Hence, data fusion problems are abundant i…
Logistic principal component analysis via non-convex singular value thresholding
Yipeng Song, Johan A. Westerhuis, Age K. Smilde
Multivariate binary data is becoming abundant in current biological research. Logistic principal component analysis (PCA) is one of the commonly used tools to explore the relations…
Separating common (global and local) and distinct variation in multiple mixed types data sets
Yipeng Song, Johan A. Westerhuis, Age K. Smilde
Multiple sets of measurements on the same objects obtained from different platforms may reflect partially complementary information of the studied system. The integrative analysis…