JP-7854899-B2 - Data processing device, data processing system, and data processing method

Inventors

• 長村 燎
• 成田 直幸
• 前田 知幸

Assignees

• 株式会社東芝
• 東芝デバイス＆ストレージ株式会社

Dates

Publication Date

20260507

Application Date

20220830

Claims (9)

1. Acquisition section, Processing unit and Equipped with, The acquisition unit is capable of acquiring first acquired data and first other data. The processing unit is capable of performing a first evaluation index derivation operation that derives a first evaluation index from a plurality of first regression labels and a plurality of first composite regression labels. The aforementioned multiple first regression labels are derived from multiple first machine learning models. The aforementioned multiple first machine learning models are derived from multiple first sample data, The aforementioned plurality of first synthetic regression labels are derived from a plurality of first synthetic machine learning models. The plurality of first synthetic machine learning models are derived from the plurality of first sample data and the first acquired data by first transfer learning. The plurality of first sample data are derived from the first other data, or from the first transformed other data obtained by transforming the first other data . The first acquired data includes a first feature matrix with N1 rows and D1 columns, and a first acquired label with N1 rows. The aforementioned first other data includes a first other feature matrix with Np rows and D1 columns, and a first other label with Np rows. The aforementioned N1 is an integer of 2 or more, The aforementioned Np is an integer of 2 or more, The above D1 is an integer greater than or equal to 1, The N1 is a data processing device that is smaller than the Np .
2. One of the aforementioned plurality of first sample data includes a first sample feature matrix with Ns rows and D1 column, and a first sample label with Ns rows, The aforementioned Ns is an integer of 2 or more, The data processing apparatus according to claim 1 , wherein Ns is smaller than Np.
3. The processing unit is capable of performing a first synthetic machine learning model derivation operation to derive the plurality of first synthetic machine learning models. In the first synthetic machine learning model derivation operation, the processing unit can generate one of the plurality of first synthetic machine learning models based on the first acquired data and first generated data based on one of the plurality of first sample data. The first generated data includes a first generated matrix of (Ns + N1) rows and (3 × D1) columns, and a first generated label of (Ns + N1) rows, The first generator matrix includes the first matrix data, the second matrix data, and the third matrix data. The components of the first matrix data include the row-direction combination of the first feature matrix and the first sample feature matrix, The components of the second matrix data include a matrix of zero components in row Ns and column D1, and the first sample feature matrix, joined in the row direction. The components of the third matrix data include the combination in the row direction of the first sample feature matrix and the matrix of zero components in row N1 and column D1, The data processing apparatus according to claim 2 , wherein the components of the first generated label include the combination of the first sample label and the first acquired label in the row direction.
4. The processing unit is capable of deriving one of the multiple first synthetic regression labels by inputting one of the multiple first synthetic regression matrices into one of the multiple first synthetic machine learning models during the first synthetic machine learning model derivation operation. One of the aforementioned plurality of first composite regression matrices has N1 rows (3 × D1) columns, One of the plurality of first composite regression matrices includes first composite regression matrix data, second composite regression matrix data, and third composite regression matrix data. The components of the aforementioned composite regression matrix data include the aforementioned feature matrix, The components of the aforementioned second composite regression matrix data include the aforementioned first feature matrix, The data processing apparatus according to claim 3 , wherein the components of the third composite regression matrix data include a matrix of zero components in N1 rows and D1 column.
5. Acquisition section, Processing unit and Equipped with, The acquisition unit is capable of acquiring first acquired data and first other data. The processing unit is capable of performing a first evaluation index derivation operation that derives a first evaluation index from a plurality of first regression labels and a plurality of first composite regression labels. The aforementioned multiple first regression labels are derived from multiple first machine learning models. The aforementioned multiple first machine learning models are derived from multiple first sample data, The aforementioned plurality of first synthetic regression labels are derived from a plurality of first synthetic machine learning models. The plurality of first synthetic machine learning models are derived from the plurality of first sample data and the first acquired data by first transfer learning. The plurality of first sample data are derived from the first other data, or from the first transformed other data obtained by transforming the first other data. The acquisition unit is capable of acquiring second other data, The processing unit is capable of performing a second evaluation index derivation operation that derives a second evaluation index from a plurality of second regression labels and a plurality of second composite regression labels. The aforementioned multiple second regression labels are derived from multiple second machine learning models. The aforementioned multiple second machine learning models are derived from multiple second sample data, The aforementioned multiple second synthetic regression labels are derived from multiple second synthetic machine learning models. The plurality of second synthetic machine learning models are derived from the plurality of second sample data and the first acquired data by second transfer learning. The plurality of second sample data are derived from the second other data, or second transformed other data obtained by transforming the second other data , in a data processing device.
6. The data processing device according to claim 5, wherein the processing unit can perform a designation operation to designate one of the first other data and the second other data based on the result of comparing the first evaluation index and the second evaluation index.
7. The data processing device according to claim 6 , wherein the processing unit can perform regression on other acquired data using one of the designated first other data and second other data.
8. One or more acquisition units, One or more processing units, Equipped with, The one or more acquisition units are capable of acquiring first acquired data and first other data. The one or more processing units described above are capable of performing a first evaluation index derivation operation that derives a first evaluation index from a plurality of first regression labels and a plurality of first composite regression labels. The aforementioned multiple first regression labels are derived from multiple first machine learning models. The aforementioned multiple first machine learning models are derived from multiple first sample data, The aforementioned plurality of first synthetic regression labels are derived from a plurality of first synthetic machine learning models. The plurality of first synthetic machine learning models are derived from the plurality of first sample data and the first acquired data by first transfer learning. The plurality of first sample data are derived from the first other data, or from the first transformed other data obtained by transforming the first other data . The first acquired data includes a first feature matrix with N1 rows and D1 columns, and a first acquired label with N1 rows. The aforementioned first other data includes a first other feature matrix with Np rows and D1 columns, and a first other label with Np rows. The aforementioned N1 is an integer of 2 or more, The aforementioned Np is an integer of 2 or more, The above D1 is an integer greater than or equal to 1, The N1 is a data processing system that is smaller than the Np .
9. The processing unit is instructed to perform the first evaluation index derivation operation. In the first evaluation index derivation operation, the processing unit, The first evaluation metric is derived from multiple first regression labels and multiple first composite regression labels. The aforementioned multiple first regression labels are derived from multiple first machine learning models. The aforementioned multiple first machine learning models are derived from multiple first sample data, The aforementioned plurality of first synthetic regression labels are derived from a plurality of first synthetic machine learning models. The plurality of first synthetic machine learning models are derived from the plurality of first sample data and the first acquired data by first transfer learning. The aforementioned plurality of first sample data are derived from first other data, or first transformed other data obtained by transforming the aforementioned first other data . The first acquired data includes a first feature matrix with N1 rows and D1 columns, and a first acquired label with N1 rows. The aforementioned first other data includes a first other feature matrix with Np rows and D1 columns, and a first other label with Np rows. The aforementioned N1 is an integer of 2 or more, The aforementioned Np is an integer of 2 or more, The above D1 is an integer greater than or equal to 1, A data processing method in which N1 is smaller than Np .

Description

Embodiments of the present invention relate to a data processing device, a data processing system, and a data processing method. For example, data from various electronic devices, such as magnetic recording and playback devices, is processed. For example, machine learning is performed through data processing. High-precision data processing is desired. Daume'III, H., "Frustratingly Easy Domain Adaptation," Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 256-263, June 2007. Figure 1 is a schematic diagram illustrating the operation of a data processing device according to the first embodiment.Figure 2 is a schematic diagram illustrating a data processing device according to the first embodiment.Figures 3(a) to 3(c) are schematic diagrams illustrating the operation of the data processing device according to the first embodiment.Figure 4 is a schematic diagram illustrating the operation of the data processing device according to the first embodiment.Figure 5 is a schematic diagram illustrating the operation of the data processing device according to the first embodiment.Figure 6 is a schematic diagram illustrating the operation of the data processing device according to the first embodiment.Figure 7 is a schematic diagram illustrating the operation of the data processing device according to the first embodiment.Figure 8 is a schematic diagram illustrating the operation of a data processing device according to the first embodiment.Figure 9 is a schematic diagram illustrating the operation of a data processing device according to the first embodiment.Figure 10 is a schematic diagram illustrating the operation of a data processing device according to the first embodiment.Figures 11(a) and 11(b) are schematic diagrams illustrating the operation of the data processing device according to the first embodiment.Figure 12 is a schematic diagram illustrating the operation of a data processing device according to the first embodiment.Figure 13 is a schematic diagram illustrating the operation of a data processing device according to the first embodiment.Figure 14 is a schematic diagram illustrating the operation of a data processing device according to the first embodiment.Figure 15 is a schematic diagram illustrating the operation of a data processing device according to the first embodiment.Figure 16 is a schematic diagram illustrating a data processing device according to an embodiment. Embodiments of the present invention will be described below with reference to the drawings. The drawings are schematic or conceptual. In this specification and in each drawing, elements similar to those described above in previous drawings are denoted by the same reference numerals, and detailed explanations are omitted where appropriate. (First Embodiment) Figure 1 is a schematic diagram illustrating the operation of a data processing device according to the first embodiment. Figure 2 is a schematic diagram illustrating a data processing device according to the first embodiment. As shown in Figure 2, the data processing device 110 according to the embodiment includes a processing unit 71. The processing unit 71 is capable of acquiring various types of data 10D. For example, the data processing device 110 may also include an acquisition unit 72. The acquisition unit 72 may acquire various types of data 10D, and the data 10D acquired by the acquisition unit 72 may be supplied to the processing unit 71. The data processing device 110 may also include a storage unit 73. The data 10D acquired by the acquisition unit 72 may be stored in the storage unit 73. The processing unit 71 may also acquire the data 10D stored in the storage unit 73 from the storage unit 73. The acquisition unit 72 is, for example, an interface. The acquisition unit 72 may be, for example, an interface for input and output. The processing unit 71 may output information I1 related to the processing result. Information I1 may be output via the acquisition unit 72 (interface). The processing unit 71 may be able to communicate with the server 74. Communication may include at least one of providing information and acquiring information. Communication may be based on any method, such as wired or wireless. Data 10D may include, for example, acquired data (e.g., first acquired data 11 and second acquired data 12). Data 10D may also include other data (e.g., first other data 51 and second other data 52). As described later, the processing unit 71 can generate various types of data (for example, first generated data 21, second generated data, first machine learning model 31, and second machine learning model 32, etc.). The generated data may be stored in the storage unit 73. For example, the memory unit 73 may include a first memory area 73a and a second memory area 73b. For instance, the first acquired data 11 and the first other data 51 may be stored in the first memory area 73a. The processing unit 71 may acquire the first acquired