Figure 6.

Dimension reduction. (a) Dialog box for projecting normal‐mode amplitudes computed by image analysis onto a space of lower dimension using PCA (here, 2 in “Reduced dimension” means projecting onto a 2D space) or one of several other dimension reduction methods selected via “Dimensionality reduction method” (the default values of parameters of these methods are provided in the help message displayable by clicking on the corresponding question mark). (b) Dimension reduction viewer allowing visualizing normal‐mode amplitudes in the low‐dimensional space (here, a 2D space specified by axes 1 and 2) as well as opening the clustering (grouping) and trajectories tools. (c) Example of projecting normal‐mode amplitudes onto a low‐dimensional space (here, 2D space specified in panel b). In panel a, the methods available for the dimension reduction are PCA, Kernel PCA, Probabilistic PCA, Local Tangent Space Alignment (LTSA), Linear LTSA, Diffusion Map, Linearity Preserving Projection, Laplacian Eigenmap, Hessian Locally Linear Embedding, Stochastic Proximity Embedding, and Neighborhood Preserving Embedding