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Sato-Ilic Lab
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A lab that analyzes multidimensional data using statistical science and soft computing.
AboutAbout the lab
Drawing out features from multidimensional data.
The Sato-Ilic Lab studies methods for analyzing multidimensional data from the perspectives of statistical science and soft computing.
Building on data mining and multidimensional data analysis, it works on artificial intelligence-based analysis of high-dimensional, small-sample data and its societal applications.
It develops methods for mixed data and high-dimensional, small-sample data, such as principal component analysis based on fuzzy clustering.
Key terms
- Fuzzy clustering
- A classification method that does not assign each data point to a single group, but divides the data by its degree of membership in each group.
- Principal component analysis
- A data analysis method that condenses many variables into a small number of axes while retaining as much information as possible.
- High-dimensional, small-sample data
- Data in which the number of observations is small compared with the number of variables examined.
NewsLab news
View More ↗ResearchResearch themes
The lab studies methods for capturing the features of high-dimensional data using ideas from statistical science and soft computing. Building on data mining and multidimensional data analysis, it advances artificial intelligence-based analysis and its social applications.
*Illustrative imageDimension Reduction for Mixed Data
The lab studies how to apply weighted principal component analysis based on fuzzy clustering to mixed data. It also works on dimension reduction of fuzzy interval data using deep learning.
*Illustrative imageExtracting Individuality from High-Dimensional, Small-Sample Data
The lab studies methods for extracting the individuality of each object from high-dimensional, small-sample data, which has many variables but few observations. It uses classification based on fuzzy clustering and principal component analysis with cluster scales.
*Illustrative imageAutomatic Coding of Survey Data
The lab studies automatic coding, which assigns classification codes automatically to the income and expenditure items of the Family Income and Expenditure Survey. It compares classification methods such as support vector machines based on fuzzy clustering.
*Illustrative imageVisualizing Learning Data and Educational Applications
The lab studies how to map students' usage patterns of the digital ecosystem using kernel principal component analysis, and use the results in course design. It also works on educational applications of methods that visualize mixed data simultaneously.
MembersFaculty
- ProfessorMika Sato-Ilic
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Scatter plot” UCRL / Public domain (cropped)
- Dimension Reduction for Mixed Data: “Racks Amravati Data Center” PiDatacenters / CC BY-SA 4.0 (cropped)
- Extracting Individuality from High-Dimensional, Small-Sample Data: “Solar Data Analyst at Work in Kenya” Dwalsh3 / CC BY-SA 4.0 (cropped)
- Automatic Coding of Survey Data: “A household goods shop in Naha 2015” Dquai / CC BY-SA 4.0 (cropped)
- Visualizing Learning Data and Educational Applications: “Ohio University Lecture Hall” Garden Sprite / CC0 (cropped)
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- Related lab (繁野研究室): “Flight departure board at Canberra Airport, November 2022” Kgbo / CC BY-SA 4.0 (cropped)
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