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Yata Lab
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The lab explores high-dimensional, small-sample data using multivariate analysis and asymptotic theory.
AboutAbout the lab
Studying the properties of high-dimensional data with statistical theory
The Yata Lab studies how to handle high-dimensional data with statistical theory.
Its research areas are multivariate analysis, sequential analysis, analysis of high-dimensional small-sample data, and asymptotic theory.
The lab belongs to the Information Mathematics field of the Mathematics Degree Program, and has co-authored papers with Professor Makoto Aoshima of the same field.
Key terms
- high-dimensional small-sample data
- Data in which the number of samples is small compared with the number of measured variables
- asymptotic theory
- A theory that mathematically examines how statistical methods behave as the number of observations or variables grows large
- multivariate analysis
- A general term for methods that statistically analyze data with many variables together
- principal component analysis
- A method that summarizes the information in many variables by condensing it into a small number of composite variables
- cluster analysis
- A method that divides similar data into groups (clusters)
ResearchResearch themes
The Yata Lab's research areas are multivariate analysis, sequential analysis, analysis of high-dimensional small-sample data, and asymptotic theory. Many of its papers in recent years are co-authored with Professor Makoto Aoshima and others, and cover clustering of high-dimensional data, estimation and testing of covariances and correlations, and principal component analysis.
*Illustrative imageClustering of High-Dimensional Data
The lab mathematically investigates how methods that divide data into groups, such as k-means and hierarchical clustering, behave on high-dimensional data.
Estimation and Testing of Covariance and Correlation
The lab studies methods of estimation and testing for covariance matrices and correlations, which express relationships among variables, in high-dimensional data.
*Illustrative imagePrincipal Component Analysis and Outlier Detection
The lab studies how to use principal component analysis, which summarizes data with many variables, on high-dimensional data. It also works on outlier tests that use principal component analysis.
MembersFaculty
- ProfessorKazuyoshi Yata
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: “Climate data analysis using tSNE method” Orlovma / CC BY 4.0 (cropped)
- Clustering of High-Dimensional Data: “Spectral clustering vs k means” BogdanShevchenko / CC BY-SA 4.0 (cropped)
- Principal Component Analysis and Outlier Detection: “GreenAmongRed” Pereru / CC BY-SA 3.0 (cropped)
- Related lab (
青嶋研究室): “Mechanical-calculator-Brunsviga-15-01” CEphoto, Uwe Aranas / CC BY-SA 3.0 (cropped) - Related lab (大谷内 奈穂 研究室): “Jeffreys prior probability for the beta distribution - J. Rodal” Dr. J. Rodal / CC BY-SA 3.0 (cropped)
- Related lab (Scott Carnahan 研究室): “Math01” MathKnight ✡ (Talk) No machine-readable author provided. MathKnight assumed (based on copyright claims). / CC BY 2.5 (cropped)
- Related lab (小池 貴之 研究室): “Complex plot for derangement function re and im between -9 to 9” A2569875 / CC BY-SA 4.0 (cropped)
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