Figure 5
From: Sugariness prediction of Syzygium samarangense using convolutional learning of hyperspectral images
The workflow shows how t-SNE evaluates the learning results. First, the matrix with the size is obtained by feeding the dataset \(X\) to proposed deep learning models and collecting the layer's outputs before the last regression output layer. Then the matrix is scaled to \(N\) data points to visualize the t-SNE result. The process of t-SNE is as same as the process in Fig. 4.
