We use a hexapod robot to automatically acquire labeled data points which we use to train 3D CNN architectures for multi-output regression.
Assign each of the mixture components to one of the classes +, − by determining to what degree, in terms of their posteriors, the labeled data points contribute to it.
On the right-hand side of the above equation, the first term is the smoothness function which requires the neighboring data points to belong to the same class, while the second term is the fitting function which limits the labeled data points in order to be consistent with their original labels.
Label data points as core, border, and noise points and remove noise points.
When training IDRN classifier, for each node i with a set of true labels (T_i), we transform it into a set of single-label data points, i.e., ({ langle {mathbf{X }}_{{mathcal {N}}_i}, c rangle | c in T_i}).
After that, we transform each node i with a multi-label set (T_i) into a set of single-label data points and use the multinomial naive Bayes framework to count the values of N, (N_c) and (N_{mathrm{kc}}) as shown from line 3 to line 12 in Algorithm 1.
Let f : X→ Y be a function that labels data points in G. Cheeger regularization is defined as (3) where D=diag(d i ) is a diagonal matrix with d i =∑ j w ij, L= D− W is the Laplacian of G, 1=(1,…, 1) T and β is the non-negative weight.
In the next iteration (iteration 2) in Figure 4, the labeled dataset is increased by the two pseudo-labeled data points L = { (x 1, + 1 ), (x 2, + 1 ), (x 3, - 1 ), (x 4, - 1 ) }, and the unlabeled data set is decreased to U = { x 3 }.
SSL Co-training increases the performance of an ordinary SSL thanks to the pseudo-labeled data points.
Furthermore, to obtain pixel level classification, each pixel of a FLIM image was considered to be a data-point, yielding a labeled data set of 21 × 60 × 60 data-points.
Once the hierarchical data abstractions are learnt from unsupervised data with Deep Learning, more conventional discriminative models can be trained with the aid of relatively fewer supervised/labeled data points, where the labeled data is typically obtained through human/expert input.
