Fujitsu Develops AI Tech for High-Dimensional Data Without Labeled Training Data

In recent years, there has been a surge in demand for AI-driven big data analysis in various business fields. AI is also expected to help support the detection of anomalies in data to reveal things like unauthorized attempts to access networks, or abnormalities in medical data for thyroid values or arrhythmia data. Data used in many business operations is high-dimensional data. As the number of dimensions of data increases, the complexity of calculations required to accurately characterize the data increases exponentially, a phenomenon widely known as the "Curse of Dimensionality"(1). In recent years, a method of reducing the dimensions of input data using deep learning has been identified as a promising candidate for helping to avoid this problem. However, since the number of dimensions is reduced without considering the data distribution and probability of occurrence after the reduction, the characteristics of the data have not been accurately captured, and the recognition accuracy of the AI is limited and misjudgment can occur (Figure 1). Solving these problems and accurately acquiring the distribution and probability of high-dimensional data remain important issues in the AI field.