Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A feature learning method and system based on a parallel autoencoder

An automatic coding machine and feature learning technology, which is applied in neural learning methods, special data processing applications, biological neural network models, etc. The waste of space, the effect of ensuring the correctness of the calculation, and improving the training speed

Active Publication Date: 2018-06-26
INST OF COMPUTING TECH CHINESE ACAD OF SCI
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when the data scale increases, the computing power required by this parallel autoencoder scheme will increase non-linearly. Therefore, when this scheme faces massive data computing tasks, it is often difficult to meet the requirements in terms of data processing efficiency and time.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A feature learning method and system based on a parallel autoencoder
  • A feature learning method and system based on a parallel autoencoder
  • A feature learning method and system based on a parallel autoencoder

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0052] figure 2 A system platform for realizing a parallel automatic encoding machine according to an embodiment of the present invention is shown, and the system platform includes a manager (Manager), multiple workers (Workers) and multiple data slice storage devices. The management machine can be deployed in the cloud, and it is connected to each working machine separately. Each working machine is connected to a data slice storage device respectively. Among them, the management machine is mainly used to collect data and distribute and schedule tasks for the worker machines. The worker is responsible for completing specific calculations. Multiple worker machines can work in parallel. It should be noted that this system structure is only exemplary, and in other embodiments of the present invention, other types of system platform frameworks can also be used, as long as the system platform framework includes a management machine and a plurality of working machines interconne...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The present invention provides a feature learning method based on a parallel automatic encoding machine, comprising: 1) a management machine executes a Map operation, plans tasks for each working machine and distributes the tasks to each working machine; wherein, the tasks of each working machine are consistent, Both are based on the input data to train the weight matrix of the automatic encoding machine; the weight matrix contains the ownership value of the automatic encoding machine; 2) each working machine reads the corresponding part of the data of the working machine 3) Each working machine executes the tasks distributed by the management machine in parallel, trains the weight matrix of the autoencoder, and then each working machine feeds back the weight matrix it has trained to the management machine; 4) the management machine Execute the Reduce operation, and perform an arithmetic average on the weight matrix fed back by each working machine. The invention can realize feature learning more efficiently, and can reduce the time complexity of data processing of the parallel automatic encoding machine from quadratic complexity to linear complexity.

Description

technical field [0001] The present invention relates to the technical field of data mining. Specifically, the present invention relates to a feature learning method and system based on a parallel automatic encoding machine, which can be applied to multiple fields such as finance, communication, image processing, Web data analysis, and big data analysis. industry. Background technique [0002] With the informatization and networking of social development, information technology has increasingly affected all aspects of social life, and the development of computers has greatly improved social production efficiency. More and more people rely on computers and the Internet to improve living standards and work efficiency, continuously transform society and improve the quality of life. However, with the gradual application of computers in various fields, a large amount of information and data will inevitably be generated. And these information and data are not just data, the infor...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/04G06N3/08G06F17/30
CPCG06F16/2471G06N3/08G06N3/045
Inventor 庄福振钱明达申恩兆敖翔罗平何清
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com