Latent semantic learning method for multi-view multi-label data

A learning method and multi-label technology, applied in the field of latent semantic learning for multi-view and multi-label data, can solve problems such as low-dimensional latent semantic space, achieve the effect of overcoming shortcomings and limitations, and solving the problem of multi-view complementarity

Active Publication Date: 2019-09-20
GUANGDONG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0008] In order to overcome the defect that the existing technology cannot effectively fuse multi-view and multi-label information to learn to ob...

Method used

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  • Latent semantic learning method for multi-view multi-label data

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Embodiment 1

[0046] Such as figure 1 As shown, the implementation of the present invention provides a hidden semantic space learning method that fuses multi-view and multi-label information, including:

[0047]Read the multi-view and multi-label information, and then learn the projection matrix of the multi-view information. The present invention involves the learning of multiple views, which contains multiple sets of variables that need to be updated. At the same time, the model has orthogonal constraints on the learned latent semantic space. Therefore, alternate direction multiplier method and bregman iteration are introduced to optimize the solution. ADMM adopts the method of decomposing coordinates to solve the optimization problem into smaller local sub-problems, and then the solutions of these local sub-problems are used to restore the solution of the original large-scale optimization problem in a coordinated manner. For each view-related variable update, stop updating when the erro...

Embodiment 2

[0049] The hidden semantic space learning method for fusing multi-view and multi-label information provided in this embodiment is the same as that in Embodiment 1, and only the steps in the method are further limited.

[0050] Step S1: Data preprocessing, read multi-view and multi-label data for preprocessing, the preprocessing part removes the stop words of the text data and vectorizes the data through the TF-IDF algorithm, and uses the feature vectorization result obtained from the preprocessing as the method input of;

[0051] Step S2: Build a latent semantic learning model for multi-view and multi-label data; based on the principle of mapping data to low-dimensional space to minimize reconstruction errors and retain as much information as possible, a classifier that integrates different perspectives and characteristics is constructed, and at the same time Combined with the correlation of multiple tags, the present invention optimizes the following constrained objective fun...

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Abstract

The invention provides a latent semantic learning method for multi-view multi-label data, which comprises the following steps of: reading multi-view multi-label data as the input of a preprocessor, then carrying out data preprocessing, inputting a preprocessing result into a trainer, and learning to obtain a latent semantic subspace and a trained model; when the label which is not labeled with the multi-view data is predicted, preprocessing the unlabeled multi-view data, and then inputting the unlabeled multi-view data into the trained model for prediction to obtain a multi-label category. The invention provides a learning and multi-label classification method fusing multi-view angle information. Input features and output multi-labels are mapped into a common potential semantic subspace. According to the method, the problem of dimensionality disasters caused by high-dimensional sparse data can be solved, and meanwhile, multi-view information is fused to classify multi-label data.

Description

technical field [0001] The present invention generally relates to matrix analysis, orthogonal constraint methods and optimization methods in networks, and more specifically, relates to a latent semantic learning method for multi-view and multi-label data. Background technique [0002] In recent years, the wave of digitalization with big data, Internet of Things, artificial intelligence, and 5G as its core features is sweeping the world, which brings massive amounts of data in various fields. Therefore, the classification problem in the image field and the automatic labeling of video concepts have been receiving keen attention from academia and industry. With the growing size and number of music databases, emotional retrieval of music has become an important task for various applications such as song selection for mobile devices, music recommendation systems, TV broadcasting programs, and music therapy. Multi-view multi-label learning is needed in these scenarios, because in...

Claims

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Application Information

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IPC IPC(8): G06F16/35G06F16/33G06F17/27
CPCG06F16/35G06F16/3344G06F40/30
Inventor 温雯韦滨蔡瑞初郝志峰陈炳丰
Owner GUANGDONG UNIV OF TECH
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