<p>Two panels showing the embedding of trajectory 1 from the internal coordinate space (described in the paper) to a 2D space obtained by PCA (left) and nMDS (right). PCA results were not stable when binning time was reduced, nMDS was found to be more stable. Additionally, the first two PCA axes capture only 40 of the total amplitude fluctuation in the data.</p
Distance matrix between simulations uses PCA coordinates by default. Projection coordinates are diff...
<p>The first three of four steps show how 2-step PCA is applied to the data, where the concatenation...
<p>Same methods were employed as with the first PC. The 9 best correlated neural features were selec...
<p>Two panels showing the embedding of trajectory 1 from the dihedral angle space to a 2D space obta...
<p>Correlation coefficients between 2D and 3D axes obtained by applying PCA to nMDS results on all v...
Top: PCA embedding with linear baseline and nonlinear aggregated velocity directions, as well as gro...
a. Linear PCA embedding of ground truth velocities. b. Linear PCA embedding of inferred velocities. ...
<p><b>A</b>,<b>B</b>) Summary of principal motions for SMX and no SMX trajectories from PCA of indiv...
Top: PCA embedding with linear baseline and nonlinear aggregated velocity directions. Bottom: UMAP a...
<p>(a) Interdomain angles shown here for one set of AMBER simulations (sim1, sim2, sim5). The horizo...
A. 2D scatter plot showing each fly as a dot, and the mean and standard error of the factor loadings...
Item does not contain fulltextHuman movements, recorded through kinematic data, can be described by ...
(A) PCA scree plot: dot shows the cumulative explained variance of the principal components; the bar...
<p>Initially, PCA calculations were carried out with all the CycT1 models including WT, and CycT1 mu...
Principal Component Analysis (PCA) is a usual method in multivariate analysis to reduce data dimensi...
Distance matrix between simulations uses PCA coordinates by default. Projection coordinates are diff...
<p>The first three of four steps show how 2-step PCA is applied to the data, where the concatenation...
<p>Same methods were employed as with the first PC. The 9 best correlated neural features were selec...
<p>Two panels showing the embedding of trajectory 1 from the dihedral angle space to a 2D space obta...
<p>Correlation coefficients between 2D and 3D axes obtained by applying PCA to nMDS results on all v...
Top: PCA embedding with linear baseline and nonlinear aggregated velocity directions, as well as gro...
a. Linear PCA embedding of ground truth velocities. b. Linear PCA embedding of inferred velocities. ...
<p><b>A</b>,<b>B</b>) Summary of principal motions for SMX and no SMX trajectories from PCA of indiv...
Top: PCA embedding with linear baseline and nonlinear aggregated velocity directions. Bottom: UMAP a...
<p>(a) Interdomain angles shown here for one set of AMBER simulations (sim1, sim2, sim5). The horizo...
A. 2D scatter plot showing each fly as a dot, and the mean and standard error of the factor loadings...
Item does not contain fulltextHuman movements, recorded through kinematic data, can be described by ...
(A) PCA scree plot: dot shows the cumulative explained variance of the principal components; the bar...
<p>Initially, PCA calculations were carried out with all the CycT1 models including WT, and CycT1 mu...
Principal Component Analysis (PCA) is a usual method in multivariate analysis to reduce data dimensi...
Distance matrix between simulations uses PCA coordinates by default. Projection coordinates are diff...
<p>The first three of four steps show how 2-step PCA is applied to the data, where the concatenation...
<p>Same methods were employed as with the first PC. The 9 best correlated neural features were selec...