Neural Network Topic Model Parameter Estimation
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Solution Overview
Problem
Topic models require a large amount of data for learning, making it difficult to estimate parameters effectively from smaller datasets.
Innovation Solution
A learning method that uses a parameter estimation device to learn and estimate topic model parameters from a smaller amount of data by employing a learning phase with neural networks to generate auxiliary and evaluation data, allowing for parameter estimation even with limited input data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a topic model is used for analyzing discrete data, then the usefulness is confirmed in various applications such as document analysis, but a large amount of data is required for learning
Solution Approach 1:
The patent applies preliminary action by pre-training a neural network model on a large dataset to learn the relationship between input data and topic model parameters. This pre-learned model is then used to estimate parameters from smaller datasets, eliminating the need to process the entire large dataset each time topic model learning is required.
Solution Approach 2:
The patent introduces an intermediary neural network model that acts as a bridge between the large training dataset and the smaller target datasets. The neural network learns to map input data directly to topic model parameters, serving as an intermediary that eliminates the need for direct processing of large amounts of data during the actual topic model learning process.
2Measurement precision
If a large amount of data is used for learning topic model parameters, then the estimation accuracy is improved, but the learning time and computational cost increase
Solution Approach 1:
The patent performs the computationally intensive learning process in advance by training a neural network on a large dataset. Once the neural network is trained, it can quickly estimate topic model parameters from smaller datasets without requiring repeated processing of the entire large dataset, thus reducing learning time while maintaining accuracy.
Solution Approach 2:
The patent creates a copied representation of the knowledge contained in the large dataset by training a neural network model. This neural network model serves as a compact copy that encapsulates the statistical relationships learned from the large dataset, enabling fast parameter estimation without accessing the original large dataset during inference.
3Quantity of substance
If traditional topic model learning is applied to small datasets, then the data requirement is reduced, but the learning performance deteriorates
Solution Approach 1:
The patent introduces a neural network model as an intermediary that was pre-trained on a large dataset. This intermediary model compensates for the limitations of small datasets by providing prior knowledge learned from the large training dataset, thereby maintaining learning performance even when only small target datasets are available.
Solution Approach 2:
The patent changes the approach from directly learning topic model parameters from data to using a neural network that has learned optimal parameter estimation patterns. The neural network's weights and biases are adjusted during pre-training to capture the underlying data distribution, enabling effective parameter estimation from small datasets without requiring the same amount of data as traditional methods.
Data Source
AI summary
A learning method according to an embodiment causes a computer to execute: an input step of inputting a plurality of data sets; and a learning step of learning, based on the plurality of input data sets, an estimation model for estimating a parameter of a topic model from a smaller amount of data than an amount ot data included in the plurality of data sets.