Research Article
Semisupervised Deep Embedded Clustering with Adaptive Labels
Algorithm 1
Deep semiclustering with adaptive labels.
| Input: the training dataset , the number of clusters k, the iteration maximum maxiter, and the training threshold. | | Output: the cluster assignment Q, the cluster centroids , and the nonlinear mapping . | | Begin | | Pretraining computing: | | To construct the deep code network. | | To initialize network parameters based on the normal distribution. | | To train each layer of the deep code network based on the denoising autoencoder strategy. | | To connect each pretrained layer and fine-tune network parameters in an end-to-end manner. | | To use pretrained deep code network to map raw data into the latent space for obtaining feature . | | To use K-means to initialize centroids based on feature . | | Clustering computing with adaptive labels: | | To use equations (7) and (8) to compute cluster assignment Q and target assignment P. | | To compute . | | To use equation (10) for constructing the label list. | | To dynamically rectify labels based on the adaptive label algorithm. | | To compute the loss based on equation (11). | | To update network parameters and centroids. | | End |
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