A Supervised Online Topic Model Learning Method Based on Sparse Implicit Feature Expression

Abstract

The invention discloses a supervised online topic model learning method based on sparse implicit characteristic expression, and relates to the field of data mining and machine learning. The method comprises the following steps of: carrying out sparse expression based implicit characteristic extraction on a document in a training set and each word in the document by an online learning method, so as to obtain a plurality of groups of characteristic vectors; training a classifier according to the characteristic vector of the training set and the class information of the document in the training set so as to obtain the characteristic vector of the classifier, wherein each type of the characteristic vector of the classifier corresponds to the type of the document in the training set; extracting the characteristic vectors of all the documents to be recognized; and calculating inner products on the characteristic vectors of the documents to be recognized and the characteristic vector of each type of the classifier, wherein that the maximum value of the inner product corresponds to the type of the training set is considered as the recognition result of the documents to be recognized. According to the supervised online topic model learning method based on the sparse implicit characteristic expression, the speed in model training is greatly increased by adopting the online learning way, and the accuracy rate of classification can be increased by utilizing the supervision information.

Description

technical field [0001] The invention relates to the technical fields of data mining and machine learning, in particular to a supervised online topic model learning method based on sparse implicit feature expression. Background technique [0002] The implicit topic model has shown obvious advantages in mining the semantic information of documents and processing complex document structures. In recent years, using the implicit topic model to efficiently mine the structure of large-scale documents and stream input documents has become a research field in this field. hotspot. [0003] At present, the existing methods of mining the semantic structure of documents using implicit topic models are mainly based on probabilistic models. Among the many models, the representative ones are Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Indexing (PLSI) and Latent Dirichlet Allocation (LDA). The main problems to be solved by using the implicit topic model to mine the semant...

AI Technical Summary

Problems solved by technology

However, these techniques cannot effectively handle large-scale document and streaming document input, and at the same time effectively control the sparsity of implicit features in topic models.

Images