# Graphs from Features: Tree-Based Graph Layout for Feature Analysis
## 5 Results
### Selecting Features of Interest by Relevance
A new cycle of analysis started with the graph in Figure 6a to which edges that indicate low similarity (yellow and orange edges) were added. Edge bundling suggests two distinct regions that could be used to select or de-select features from. In Figure 6b,c we have de-selected one feature in each graph layout. Figure 6d illustrates the selection of features through the relevance bar and their effects on the projection of the data instances as shown in Figure 6e-h. The projections created from the selected features are displayed in Figure 6i-l.  
A different course of action was used in the analysis illustrated in Figure 7. The ranking bar was used to initially select 45 out of the 141 features in the data after filtering. Then we searched for highly connected vertices with high similarity. In this process, we de-selected some features manually and aggregated others, improving the projection (as measured by the silhouette coefficient.) The visual, finer inspection of feature relations represented by edges allows improving the selection of features beyond of what is possible using the automatic tools. When selecting and de-selecting features, sometimes vertices in close visual proximity prevent a proper interpretation of size and color. The Sunburst visualization of the tree, such as presented in Figure 7b,f yields an easier interpretation of such ambiguities by showing more clearly the difference between features with basis on area and color properties of the nodes. Figure 5: Soundscape ecology data for bird and insect classes, initially with 238 features. (**a–d**) Similarity MSTs displaying an increasingly larger set of features selected by ranked relevance (Pearson correlation), with high similarity graph edges. (**e–h**) Circular histograms of the selected features. (**i–l**) LSP projections (cosine dissimilarity) of the 1662 data points from the space of selected features on 2D and silhouette values. In the figure, a graph is related to the projection and to the circular histogram in the same column. Figure 6: _Cont._  
Figure 6: Soundscape ecology data of bird and insect classes with 141 features. **(a,d)** Similarity MSTs with automatically selected features and graph edges with low similarity. **(b,c)** MSTs with manually de-selected features and graph edges with low similarity. **(e–h)** LSP projection of 1662 data points from the space of selected features on 2D and silhouette coefficients. **(i–l)** Circular histogram visualization of the selected features. Figure 7: Soundscape ecology data with 141 features from bird and insect classes. **(a)** MST with additional graph edges highlighting 45 features selected automatically by ranked relevance and **(b)** Sunburst visualization of the features. **(c)** Circular histogram of the selected features. **(d)** The LSP projection of the 1662 data points from the space of selected features on 2D and its silhouette coefficient. **(e–h)** The same types of layouts for 45 features selected by a combination of calculated rank and interaction. **Case 2: Feature selection to find relevant features in and Image collection. **  
The Corel 1k dataset contains 1000 color images in 10 classes: people, beaches, buildings, buses, dinosaurs, elephants, flowers, horses, mountains, and foods. For this test case, 150 descriptors related to color and texture were used. Figure 8 shows the results obtained using our method. Minimum spanning trees of features are shown in Figure 8a-c, with an increasingly larger set of features selected automatically via the relevance bar. Figure 8d shows an MST with a set of features manually selected by interacting with the graph. Figure 8e-h show a radial display of the selected features. From each selection, a t-SNE projection of the data is shown in Figure 8i-l, together with their silhouette coefficients. Through interacting and visually exploring this dataset we were able to improve class separation through the successive, incremental selection of features using the relevance bar, as indicated by the silhouette coefficient.