Multimodal data subspace clustering method based on global consistency and local topology

A local topology, multi-modal technology, applied in the computer field to achieve good clustering performance and enhance robustness

Inactive Publication Date: 2015-12-16
INST OF AUTOMATION CHINESE ACAD OF SCI
View PDF2 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, although some current methods can improve the clustering performance of multimodal data to a certain extent, how to

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
  • Multimodal data subspace clustering method based on global consistency and local topology
  • Multimodal data subspace clustering method based on global consistency and local topology
  • Multimodal data subspace clustering method based on global consistency and local topology

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0009] The method for clustering multimodal data subspaces based on global consistency and local topology provided by the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0010] figure 1 The flow chart of the multimodal data subspace clustering method based on global consistency and local topology provided by the embodiment of the present invention.

[0011] refer to figure 1 , in step S101, the Laplacian matrix corresponding to each modality data is obtained.

[0012] In step S102, a multimodal data subspace clustering model is constructed according to the Laplacian matrix.

[0013] In step S103, the self-expression matrix corresponding to each modality data is obtained through the multimodal data clustering model.

[0014] In step S104, a first self-expression matrix is ​​selected from the self-expression matrices corresponding to each modality data.

[0015] Here, the first self-expression matrix is ​​...

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 invention provides a multimodal data subspace clustering method based on global consistency and local topology. The method comprises obtaining a Laplacian matrix corresponding to each piece of modal data, establishing a multimodal data subspace clustering model according to the Laplacian matrixes, obtaining a self-expression matrix corresponding to each piece of modal data through the multimodal data subspace clustering model, selecting the first self-expression matrixes from all the self-expression matrixes of the various pieces of modal data, and clustering the first self-expression matrixes to obtain a clustering result. The multimodal data subspace clustering method based on global consistency and local topology is capable of obtaining better clustering performance and enhancing the robustness.

Description

technical field [0001] The invention relates to the field of computers, in particular to a multi-modal data subspace clustering method based on global consistency and local topology. Background technique [0002] With the development of science and technology and the increasing popularity of the Internet, the collection of data in modern society has become easier and easier, and the amount of data is increasing day by day. At the same time, data is becoming more and more diverse, especially various multimodal data more and more common. Learning methods based on multi-modal data have also received more and more attention and research. Compared with single-modal data, multi-modal data can provide more and more complex information, so learning based on multi-modal data Models generally perform better and have better statistical properties. [0003] In the field of multimodal learning, multimodal data clustering has received extensive attention and development due to its abili...

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
IPC IPC(8): G06K9/62G06F17/30
CPCG06F16/35G06F18/23
Inventor 赫然胡包钢樊艳波
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap