US 12,361,284 B2
Machine learning apparatus and method for machine learning by using tensor including plurality of axes as input
Ryo Ishizaki, Kawasaki (JP)
Assigned to Fujitsu Limited, Kawasaki (JP)
Filed by FUJITSU LIMITED, Kawasaki (JP)
Filed on Jul. 9, 2021, as Appl. No. 17/371,823.
Claims priority of application No. 2020-171167 (JP), filed on Oct. 9, 2020.
Prior Publication US 2022/0114442 A1, Apr. 14, 2022
Int. Cl. G06N 3/08 (2023.01); G06F 18/20 (2023.01); G06F 18/211 (2023.01); G06F 18/213 (2023.01)
CPC G06N 3/08 (2013.01) [G06F 18/211 (2023.01); G06F 18/213 (2023.01); G06F 18/29 (2023.01)] 15 Claims
OG exemplary drawing
 
1. A non-transitory computer-readable recording medium having stored therein a machine learning program executable by one or more computers, the machine learning program comprising:
an instruction for collecting, from a memory, graph information including a plurality of nodes and data representing attributes of the plurality of nodes, the attributes being a hierarchical structure,
an instruction for generating a tensor being three dimensional structured data and comprising a first axis, a second axis, and a third axis, the first axis and the second axis representing relationships of the plurality of nodes included in the graph information, the third axis representing separately first data included in a first layer of the hierarchical structure and second data included in a second layer of the hierarchical structure, and
an instruction for training a machine learning model by using the tensor being the three dimensional structured data as an input, wherein
the generating of the tensor comprises generating the third axis representing the attributes by synthesizing, for each combination of a value of the first data and a value of the second data, the first data and the second data such that the first data and the second data according to the combination are distinguished from each other, and
the training comprises inputting a core tensor obtained by performing tensor decomposition of the tensor into the machine learning model, updating a parameter of a neural network of the model, and updating a parameter of the tensor decomposition.