# 6G Cognitive Information Theory: A Mailbox Perspective
## 6 Data Life Cycle Extensions for Information Value
### Data Fusion for Data Analysis
#### 6.4.2 Data-Fusion Technologies
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Data-Fusion Technology** & **Description** & **Fusion Target** & **Literature** \\ \hline Cluster & Cluster analysis, K-mean value, hierarchical clustering, density-based clustering, grid-based clustering & Deep information mining & [60; 61] \\ \hline Dimension reduction & Principal component analysis, multidimensional scaling analysis, singular value analysis, principal component regression, partial least squares regression & Improving data quality, deep information mining & [62; 63] \\ \hline Correlation analysis & Correlation rules & Multi-source data integration & [60; 61] \\ \hline Decision fusion & Neural network, bagging, lifting, random forest & Deep information mining, strengthening decision-making and estimation & [64; 65] \\ \hline Neural network & Artificial neural network, perceptron, back propagation, radial basis function network, convolutional neural network, recursive neural network, stacked auto-encoder, deep belief network & Deep information mining, strengthening decision-making and estimation & [60; 61] \\ \hline Reinforcement learning & Monte-Carlo learning, Q-learning, strategy gradient algorithm, reinforced deep learning & Deep information mining, strengthening decision-making and estimation & [66; 67] \\ \hline Label-less learning & Mixed tagless learning & Multi-source data integration, deep information mining, strengthening decision-making and estimation & [68] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cont. the current time. This method is suitable for applications where data fusion and prediction are required. 4) Stacked auto-encoder (SAE): The auto-encoder can be regarded as a special ANN. There are only input sample data during the network-training stage, but there is no corresponding tag data. The input data is reconstructed by using the output data of the auto-encoder, and a comparison is made with the original input data. After many iterations, the objective function value is gradually minimized, i.e., the reconstructed input data is as close as possible to the original input data. An auto-encoder is self-supervised learning and is categorized as unsupervised learning. An SAE is a neural network consisting of several hidden layers between the input layer and the output layer where each hidden layer corresponds to an auto-encoder. 5) Reinforcement learning: Reinforcement learning is a method where the model gives some feedback when the data input is conducted. The supervised learning algorithm simply determines whether the input data obtains the correct output to evaluate the model. In contrast, with reinforced learning, users are expected to take appropriate action to extract useful information from the input data to improve accuracy. Under reinforced learning, it is necessary to formulate a strategy that can correlate with the prediction model and corresponding actions will be taken with that strategy. Input data directly affects the model after feedback, and associated adjustments are made by the model according to the feedback results. Some application scenarios are dynamic systems and robot behavior controlling. Common deep learning algorithms include Monte Carlo learning, Q-L, strategy gradient algorithm, reinforcement deep learning, and so on. 6) Label-less learning: A new fusion method is proposed in [68] for a large amount of emotion-recognition applications with label-less data, i.e., label-less learning technology. For a small amount of multi-modal data with labels, the principle of entropy is applied to supplement automatic label data without human intervention in the model's training. Based on the low entropy multimodal data mutual authentication algorithm, the unlabeled data is tagged independently and added to the training model, which further improves the accuracy of the model and realizes the utilization of unlabeled data.