US 12,253,993 B2
Data dimensionality reduction method, computer program, and data dimensionality reduction device
Ryota Maeno, Tokyo (JP)
Assigned to UACJ CORPORATION, Tokyo (JP)
Appl. No. 18/573,981
Filed by UACJ CORPORATION, Tokyo (JP)
PCT Filed Jun. 10, 2022, PCT No. PCT/JP2022/023422
§ 371(c)(1), (2) Date Dec. 22, 2023,
PCT Pub. No. WO2022/270332, PCT Pub. Date Dec. 29, 2022.
Claims priority of application No. 2021-103906 (JP), filed on Jun. 23, 2021.
Prior Publication US 2024/0211457 A1, Jun. 27, 2024
Int. Cl. G06F 16/00 (2019.01); G06F 16/22 (2019.01); G06F 16/23 (2019.01)
CPC G06F 16/2264 (2019.01) [G06F 16/2237 (2019.01); G06F 16/23 (2019.01)] 12 Claims
OG exemplary drawing
 
1. A method for dimensionally reducing a group of data each represented by an m-dimensional vector in an m-dimensional (m is an integer of 3 or more) high-dimensional space to an n-dimensional (n is an integer of 2 or more and less than m) low-dimensional space, the method comprising:
a reduction step of dimensionally reducing the group of data from the high-dimensional space to the low-dimensional space using a distance function that defines a distance between any two vectors in the high-dimensional space, wherein the distance function includes p (p is an integer of m or more) first parameters;
a division step of dividing the dimensionally-reduced low-dimensional space into multiple subspaces;
an analysis step of performing a regression analysis using a regression model based on at least one belonging data for each divided subspace, wherein the regression model is represented as a function of m explanatory variables and q (q is an integer of m or more) second parameters corresponding to the m explanatory variables; and
an update step of updating the p first parameters included in the distance function based on results of the regression analysis in the multiple subspaces,
wherein the reduction step, the division step, the analysis step and the update step are repeatedly performed.