Number of non zero coefficients for each syndrome for the best glmnet model (α = .11 using all features).

<p><i>t</i>: total, <i>p</i>: points, <i>d</i>: distances, <i>ar</i>: areas and an: angles.</p><p>Number of non zero coefficients for each syndrome for the best glmnet model (α = .11 using all features).</p

Pairwise average misclassification error rate for the best <i>glmnet</i> model.

<p>Pairwise average misclassification error rate for the best <i>glmnet</i> model.</p

Importance plots <i>glmnet</i>.

<p>Visualization of simultaneous classification for syndromes. For each syndrome an importance plot (row I) and a plot visualizing classification features (row F) is provided. Importance plot assigns an importance with respect to classification to each point as described in the text. Feature plots visualize absolute regression coefficients by thickness of line segments (distances), size of points (coordinates), color of areas (areas; dark red more important than light red) and small triangles (angles; dark red more important than light red).</p

Importance weighting.

<p>Illustration of the procedure to compute importance for point δ. Contributions of point p₁, area of triangle t₁, distance d₁, and angle a₁ (blue) are weighted according to distance to δ (red). Distances to p₁, centroid c₁, midpoint m₁, vertex v₁ are used for p₁, t₁, d₁, and a₁, respectively.</p

Confusion matrix for the best <i>glmnet</i> model, α = .11, using all features.

<p>Rows indicate the percentages of predicted syndromes for each of the syndromes in the study.</p><p>Confusion matrix for the best <i>glmnet</i> model, α = .11, using all features.</p

Classification and Visualization Based on Derived Image Features: Application to Genetic Syndromes

<div><p>Data transformations prior to analysis may be beneficial in classification tasks. In this article we investigate a set of such transformations on 2D graph-data derived from facial images and their effect on classification accuracy in a high-dimensional setting. These transformations are low-variance in the sense that each involves only a fixed small number of input features. We show that classification accuracy can be improved when penalized regression techniques are employed, as compared to a principal component analysis (PCA) pre-processing step. In our data example classification accuracy improves from 47% to 62% when switching from PCA to penalized regression. A second goal is to visualize the resulting classifiers. We develop importance plots highlighting the influence of coordinates in the original 2D space. Features used for classification are mapped to coordinates in the original images and combined into an importance measure for each pixel. These plots assist in assessing plausibility of classifiers, interpretation of classifiers, and determination of the relative importance of different features.</p></div

Average misclassification error <i>glmnet</i>.

<p>Average misclassification error with 95% confidence intervals across leave-one-out cross-validation for models with different values of mixing parameter α. (a) all features (red) and only points (blue) were used and (b) all features and their squares (red) and only points and their squares (blue) were used.</p

Importance plots PCA.

<p>Visualizations analogous to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109033#pone-0109033-g005" target="_blank">figure 5</a> for PCA based classification.</p

Simultaneous average misclassification error (AME) per syndrome.

<p>Simultaneous average misclassification error (AME) per syndrome.</p

Illustration of data set.

<p>(a) Example of registered nodes. (b) Distances between coordinate pairs excluding symmetries. Numbers 1 to 48 correspond to landmarks; red: pairwise edges, excluding symmetries; black: Delaunay triangulation. Example of symmetric distances (25, 24) and (23,24).</p