Dimension reduction model training method and device and electronic equipment

A technology for model training and dimensionality reduction, applied in the field of artificial intelligence, which can solve problems such as the inability of feature data to be reduced to a specified dimension, and the prediction of newly acquired feature data.

Inactive Publication Date: 2019-05-03
树根互联技术有限公司 +5
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  • Summary
  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, the widely used dimensionality reduction method usually obtains low-dimensional feature data through a large amount of feature data through a dimensionality reduction algorithm, but this dimensionality reduction algorithm cannot predict some newly acquired feature data through dimensionality reduction algorithms, resulting in The acquired feature data cannot be reduced to the specified dimension by the dimensionality reduction algorithm

Method used

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  • Dimension reduction model training method and device and electronic equipment
  • Dimension reduction model training method and device and electronic equipment
  • Dimension reduction model training method and device and electronic equipment

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Embodiment 1

[0054] This embodiment provides a method for training a dimensionality reduction model, such as figure 1 Shown is a flowchart of a method for training a dimensionality reduction model, the method comprising:

[0055] Step S102, acquiring feature data.

[0056] Before obtaining the characteristic data, it is also necessary to obtain a characteristic data set, which includes characteristic data and non-characteristic data, and then filter the characteristic data set to obtain the characteristic data. Exemplarily, the characteristic data set can come from any technical field. The embodiment of the present invention takes construction machinery working condition data as an example. The characteristic data set can include vehicle type, working mode, gear position, working month, working time, etc. However, When construction machinery is working, some sensors may not work normally, resulting in some non-characteristic data in the feature data set, which often have a great impact on...

Embodiment 2

[0073] Corresponding to the above method embodiments, this embodiment provides a dimensionality reduction model training device, such as Figure 7 As shown, the device includes:

[0074] The first acquiring module 71 is configured to acquire feature data.

[0075] The feature result output module 72 is configured to input feature data into the dimensionality reduction algorithm, and output feature results with a preset number of levels.

[0076] The training data acquisition module 73 is configured to input feature data into the dimensionality reduction model to be trained, and acquire training data with the same number of preset levels as the feature results.

[0077] A loss function value determining module 74, configured to determine a loss function value according to the training data and feature results.

[0078] The training module 75 is used to train the dimension reduction model to be trained by using the training data, feature results and feature data until the loss...

Embodiment 3

[0086] An embodiment of the present invention provides an electronic device, such as Figure 8 As shown, the electronic device includes: a memory 81 and a processor 82. The memory 81 stores a computer program that can run on the processor 82. When the processor executes the computer program, it implements the steps provided by the above-mentioned dimensionality reduction model training method.

[0087] like Figure 8 As shown, the device further includes: a bus 83 and a communication interface 84, the processor 82, the communication interface 84 and the memory 81 are connected through the bus 83; the processor 82 is used to execute executable modules stored in the memory 81, such as computer programs.

[0088] Wherein, the memory 81 may include a high-speed random access memory (RAM, Random Access Memory), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. The communication connection between the system network element and at l...

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Abstract

The invention provides a dimension reduction model training method and device and electronic equipment, and belongs to the technical field of artificial intelligence. The embodiment of the invention provides a dimension reduction model training method, the device and electronic equipment. First, feature data is acquired, the feature data is input into a dimension reduction algorithm; a preset hierarchical number of feature results is output, the feature data are input into a to-be-trained dimension reduction model; training data of which the number is the same as that of the feature results ata preset level is obtained; a loss function value is determined according to the training data and the feature result; training data, the feature result and the feature data are used for training a to-be-trained dimension reduction model; ending training until the loss function value converges, obtaining a trained dimension reduction model. The training model can carry out dimension reduction ondata of the non-linear correlation feature, and carry out dimension reduction by the trained dimension reduction model when new feature data is obtained, so as to predict the dimension of the new feature data.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a dimensionality reduction model training method, device and electronic equipment. Background technique [0002] With the development of science and technology, many technical fields need to collect and analyze a large amount of characteristic data. However, sometimes some characteristic data are not necessary, and they need to be processed to obtain relatively more meaningful and less characteristic data. dimension process. [0003] At present, the widely used dimensionality reduction method usually obtains low-dimensional feature data through a large amount of feature data through a dimensionality reduction algorithm, but this dimensionality reduction algorithm cannot predict some newly acquired feature data through dimensionality reduction algorithms, resulting in The acquired feature data cannot be reduced to the specified dimension by the dimensional...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08
Inventor 贾杰隋楷心周子怡
Owner 树根互联技术有限公司
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