# 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  
In previous subsections we have shown that data-fusion technologies can be diversified for different data types and fusion targets. They can be divided into nine categories from two perspectives of low-level and high-level information fusion: data association, state estimation, decision fusion, classification, prediction, unsupervised machine learning, dimension reduction, statistical inference, analysis, and visualization [47]. Discussion on the advantages and disadvantages of mainstream data-fusion technologies (including K-Means, probabilistic data association (PDA), joint probabilistic data associations, distributed multiple hypothesis tests, state estimations, covariance consistency methods, decision fusion techniques, and distributed data fusion) was conducted in a previous study [43]. Summarization is conducted for data-fusion technology in this paper with two major categories (statistical methods and deep-learning methods) in machine learning as shown in Table 2, and the following methods/technologies are introduced: statistical method (clustering, correlation analysis, dimension reduction, regression analysis, decision tree, Bayesian network, and support vector machine), deep learning, reinforced learning, and label-less learning technology. _(1) Data fusion and analysis based on statistical methods:_  
1) Regression analysis: A regression algorithm is a combination method that constantly reduces the gap between the predicted value and the actual value to obtain the optimal input characteristics. Here, a linear-regression algorithm is used in the model training of continuous values, and a logistic regression algorithm is used in the model training of a discrete value or category prediction. 2) Instance-based learning: The final model of the instance-based algorithm is highly dependent on the original data. In the process of prediction, the similarity criterion is generally used to find similarities between data of the sample to be predicted and the original sample, and, finally, the prediction result is obtained. 3) Rule-based algorithm: This classification method is an extension of regression analysis and can play an important role in solving some problems. 4) Decision tree: With the decision-tree algorithm, a tree containing many decision paths is established according to the eigenvalues of the original data. For the sample to be predicted, the path decision is made according to each node in the tree, and the prediction result is finally obtained. 5) Bayes: This is an algorithm to solve classification and regression problems using the Bayes theorem, and it can be used for decision fusion. 6) Cluster analysis: The clustering algorithm divides the input sample data into different core groups and then discovers some rules among the data according to the results. 7) Correlation rules: The correlation-rules algorithm refers to obtaining a rule that can explain a certain association relation in the observed training sample, i.e., obtaining the relevant knowledge on the dependent relation between events and time and correlation and fusion of multi-source data. 8) Dimension reduction: To some extent, the dimension-reduction algorithm is like the clustering algorithm, with both aiming to discover the structure in the original data. The difference is that the dimension-reduction algorithm tries to summarize and describe most of the content represented by the original information with lower-dimensional information. 9) Support vector machine (SVM): A commonly used supervised-learning algorithm maps data from a low-dimensional space to a high-dimensional space so that linear non-fractional data in a low-dimensional space can be dividable in a high-temperature space, which is mainly used to solve classification problems. 10) Decision fusion: This is an optimization means or strategy, which usually combines several simple machine learning algorithms for reliable decision-making.