A processing method for high-order tensor data

A processing method and high-level technology, applied in the field of data processing, can solve the problems of complex feature extraction of test samples, and achieve the effect of improving image processing speed and simplifying a large amount of redundant information.

Active Publication Date: 2020-11-17
BEIJING UNIV OF TECH
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Problems solved by technology

Tucker decomposition is more complicated to extract features from test samples, so it is more suitable for clustering rather than classification

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  • A processing method for high-order tensor data
  • A processing method for high-order tensor data
  • A processing method for high-order tensor data

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[0011] like figure 1 As shown, this processing method for high-order tensor data decomposes high-order tensor data into three parts: shared subspace components, individual subspace components, and noise parts; shared subspace components and individual subspace components respectively The high-order tensor data is expressed as a linear combination of a set of tensor bases and vector coefficients; the variational EM method is used to solve the base tensor and vector coefficients; a classifier is designed to classify the samples to be tested by comparing the marginal distribution of samples.

[0012] Compared with the traditional linear discriminant method, the present invention directly acts on the tensor data and extracts common features and individual features from two spaces, and constructs a tensor data in each space by utilizing the structural characteristics of the tensor The representation of vector data can be used to extract the discriminative features of tensor samples...

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Abstract

A processing method for high-order tensor data is disclosed, which can avoid destroying the internal structure of the data during the vectorization process of the image observation sample set, is flexible and simple, can extract the discriminative features of the tensor samples, and simplifies the image observation samples Concentrating a large amount of redundant information in high-order tensor data improves the image processing speed. This processing method for high-order tensor data decomposes high-order tensor data into three parts: shared subspace components, individual subspace components, and noise parts; Quantitative data is expressed as a linear combination of a set of tensor bases and vector coefficients; the variational EM method is used to solve the base tensor and vector coefficients; a classifier is designed to classify the samples to be tested by comparing the marginal distribution of samples.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a processing method for high-order tensor data, which is mainly used for processing video sequences, RGB-D sequences and other data. Background technique [0002] With the development of science and technology, a large number of high-order tensor data are emerging, such as video sequences, RGB-D sequences, etc. Since there is often a large amount of redundant information in high-order tensor data, it is often necessary to extract features or reduce dimensions before analyzing the data. [0003] The traditional dimensionality reduction method is to vectorize the tensor data, and then use the vector dimensionality reduction method, but the vectorization process will destroy the structural relationship of the data, and the internal structure of the high-order tensor data cannot be utilized. In addition, two commonly used dimensionality reduction methods for high-ord...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06F17/16
CPCG06F17/16G06F18/213G06F18/2451G06F18/214G06V40/169G06V10/7715G06F18/24155G06F18/2132G06F18/241
Inventor 孙艳丰句福娇
Owner BEIJING UNIV OF TECH
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