A Deep Fusion Gaussian Mixture Model for Multiview Land Data Clustering

Algorithm 1

The multiview fusion Gaussian mixture model algorithm.

Input: the multiview dataset , the number of component models , the hyperparameters of deep fusion architecture

Output: patterns of the input data

1. To randomly initialize parameters of each autoencoder in the deep fusion architecture;

2. To train each autoencoder layer by layer;

3. To fine-tune the deep fusion architecture in an end-to-end manner;

4. To randomly initialize model parameters and weight coefficients of Gaussian models;

5. To compute the probability of each sample generated from each Gaussian model;

6. To compute the model parameters and weight coefficients of each Gaussian model;

7. To update model parameters and weight coefficients of Gaussian models;

8. Go to 5 until convergence, then output the probability of each data sample generated from each Gaussian model as patterns of the input data.

Algorithm 1