What is he best known for?
The fields of study he is best known for:
- Statistics
- Machine learning
- Algebra
His main research concerns Statistics, Multidimensional scaling, Algorithm, Least squares and Principal component analysis.
The Statistics study combines topics in areas such as Interval and Applied mathematics.
Yoshio Takane has included themes like Discrete mathematics and Volume in his Multidimensional scaling study.
His Algorithm research is multidisciplinary, incorporating perspectives in Mathematical optimization and Expectation–maximization algorithm.
The various areas that he examines in his Least squares study include Canonical analysis, Partial least squares regression, Correspondence analysis and Canonical correlation.
His Principal component analysis research is multidisciplinary, relying on both Multivariate statistics and Generalized singular value decomposition.
His most cited work include:
- Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features. (841 citations)
- ON THE RELATIONSHIP BETWEEN ITEM RESPONSE THEORY AND FACTOR ANALYSIS OF DISCRETIZED VARIABLES (521 citations)
- Additive structure in qualitative data: An alternating least squares method with optimal scaling features (183 citations)
What are the main themes of his work throughout his whole career to date?
Yoshio Takane mainly focuses on Statistics, Algorithm, Applied mathematics, Artificial intelligence and Principal component analysis.
Regression analysis, Contingency table, Multidimensional scaling, Multivariate statistics and Multivariate analysis are the subjects of his Statistics studies.
His work carried out in the field of Multidimensional scaling brings together such families of science as Simple and Psychometrics.
His Algorithm research integrates issues from Monotonic function and Special case.
In his study, Fuzzy logic is inextricably linked to Monte Carlo method, which falls within the broad field of Applied mathematics.
Yoshio Takane interconnects Singular value decomposition, Generalized singular value decomposition, Data mining and Missing data in the investigation of issues within Principal component analysis.
He most often published in these fields:
- Statistics (26.75%)
- Algorithm (17.83%)
- Applied mathematics (17.20%)
What were the highlights of his more recent work (between 2008-2019)?
- Statistics (26.75%)
- Algorithm (17.83%)
- Principal component analysis (15.92%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Statistics, Algorithm, Principal component analysis, Combinatorics and Matrix.
Statistics is closely attributed to Low-rank approximation in his study.
His research integrates issues of Structural equation modeling, Component analysis and Special case in his study of Algorithm.
The concepts of his Principal component analysis study are interwoven with issues in Missing data, Communication, Cognition, Posterior cingulate and Ventromedial prefrontal cortex.
His Combinatorics research incorporates elements of Square matrix, Block matrix, Projector and Inverse.
His studies deal with areas such as Reduction, Pure mathematics, Singular value decomposition, Guttman scale and Extrapolation as well as Matrix.
Between 2008 and 2019, his most popular works were:
- Multidimensional Scaling I (131 citations)
- Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition (69 citations)
- Constrained principal component analysis reveals functionally connected load‐dependent networks involved in multiple stages of working memory (56 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Algebra
His primary areas of study are Combinatorics, Principal component analysis, Inverse, Artificial intelligence and Generalized inverse.
As a part of the same scientific study, Yoshio Takane usually deals with the Principal component analysis, concentrating on Data mining and frequently concerns with Data set, Estimation theory and Function.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Partial least squares path modeling, Machine learning and Pattern recognition.
His work deals with themes such as Matrix and Schur complement, which intersect with Generalized inverse.
His Path analysis study incorporates themes from Missing data and Applied mathematics.
His Applied mathematics study incorporates themes from Component analysis and Latent variable.
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