WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (“WiMi” or the “Company”), a leading global Hologram Augmented Reality (“AR”) Technology provider, today announced that its R&D team applied machine learning algorithms to image fusion and introduced a multi-view fusion algorithm based on artificial intelligence machine learning.
Multi-view fusion algorithm based on artificial intelligence machine learning is algorithm that utilize machine learning technique for joint learning and fusion of multiple views obtained from different viewpoints or information sources. Machine learning algorithm has achieved better results in many computer vision and image processing tasks due to the strong performance shown in classification problems, feature extraction, data representation and other problems. In multi-view fusion algorithm, we can combine features from different views to obtain more comprehensive and accurate information. Information from different views can also be fused to improve the accuracy of data analysis and prediction, in addition to its ability to handle multiple data types at the same time, which can better mine the potential information of the data. Multi-view fusion algorithm studied by WiMi usually include steps such as data pre-processing, multi-view fusion, feature learning, model training and prediction.
Data pre-processing: Data pre-processing is the first step in multi-view algorithm and is used to ensure the quality and consistency of the data. Data pre-processing for each view includes steps such as data cleaning, feature selection, feature extraction and data normalization. These steps are aimed at removing noise, reducing redundant information, and extracting features that are important for the performance of the algorithm.
Multi-view fusion: Next, the pre-processed multiple views are fused. The fusion can be a simple weighted average or a more complex model integration method such as neural networks. By fusing information from different views, the advantages of different views can be synthesized to improve the performance of the algorithm.
Feature Learning and Representation Learning: Feature learning and representation learning are very important steps in multi-view algorithm. With the learned features and representations, the hidden patterns and structures in the data can be better captured, thus improving the accuracy and generalization ability of the algorithm. Commonly used feature learning methods include principal component analysis, self-encoder, etc.
Model Training and Prediction: Machine learning models are trained to learn the correlation relationship between multi-view data using data that has undergone feature learning and representation learning. Commonly used machine learning models include SVM, decision trees, deep neural networks, etc. The models obtained through training can be used for prediction and classification tasks, e.g., new incoming data can be predicted and evaluated using the trained models.
Multi-view fusion algorithm based on artificial intelligence machine learning have technical advantages such as data richness, information complementarity, model fusion capability, and adaptivity, which make multi-view algorithm has great potential and application value in dealing with complex problems and multi-source data analysis.
Each view in multi-view data provides different types of diverse data, such as text, images, sounds, etc., and each type of data has its unique features and representations, and this information can complement and enhance each other. By fusing information from different views, more comprehensive and accurate feature representations can be obtained, and the performance of data analysis and model training can be improved, and more accurate and comprehensive results can be obtained to understand and analyze the problem more comprehensively. In addition, by fusing models from different views, more powerful modeling capabilities can be obtained and overall model performance can be improved.
In addition to this, the multi-view fusion algorithm can better deal with noise and anomalies in the data by utilizing information from multiple views, reducing interference in a single view, and improving the robustness of the algorithm to noise and anomalous data. It can also adaptively select appropriate views and models for learning and prediction according to different tasks and data characteristics, and this adaptability can improve the algorithm’s adaptability and generalization ability.
Multi-view fusion algorithm has a wide range of applications in image processing, digital marketing, social media domains and IoT. By collecting data from different views and fusing these data, advertisement recommendations and intelligent applications can be made more accurately. In the field of digital marketing, multi-view fusion algorithm can utilize multiple views from user behavior, user attributes, item attributes, etc., and synthesize multiple pieces of information to improve the effectiveness of digital marketing. For example, data from user behavior, user profile data, and item attribute data can be fused to improve the accuracy and personalization of tasks such as personalized recommendations, advertisement recommendations, or information filtering. In the field of IoT, multi-view fusion algorithm can be used in smart homes and smart cities, where the management of smart homes and smart cities can be realized more accurately by collecting sensor data, environmental data, and user data from different viewpoints and fusing these data together. In the field of image processing, multi-view fusion algorithm can utilize multiple views obtained from different sensors, cameras, or image processing techniques, and synthesize multiple pieces of information to improve image processing. For example, images from different spectra, resolutions, or angles can be fused to improve the quality of an image, enhance details, and improve performance for tasks such as classification or target detection.
With the development of big data and artificial intelligence technology, in the future, WiMi will integrate deep neural networks, cross-modal learning and other technologies to continuously promote the technological innovation of multi-view fusion algorithm, integrate deep neural networks and other technologies in a deeper way, carry out deep feature extraction and fusion of multi-view data, and improve the algorithm’s performance and effect. It also realizes the effective fusion and analysis of different modal data.
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