Autoencoder Training via Probability Distribution Matching
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Solution Overview
Problem
Existing autoencoders face difficulties in matching the probability distribution of real data in the real space with that of feature data in the latent space, leading to challenges in improving data analysis accuracy, particularly in anomaly detection.
Innovation Solution
A training method for autoencoders that involves encoding input data, calculating probability distributions, adding noise to feature data, and decoding it to minimize errors and information entropy, ensuring a proportional tendency between the probability densities of input and feature data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If unsupervised learning is performed using a neural network to learn latent variables, then feature data can be extracted from real data, but the probability distribution of real data in real space cannot be effectively matched with the probability distribution of feature data in latent space
Solution Approach 1:
The patent introduces a feedback mechanism where the autoencoder's decoded output is compared with the original input data, and the probability distribution matching is used as a feedback signal to guide the training process. This feedback loop allows the system to continuously adjust the encoding and decoding parameters to improve both data analysis accuracy and probability distribution preservation.
Solution Approach 2:
The patent employs parameter changes by learning the probability distribution parameters (such as mean and variance in Gaussian distribution) of both real data and feature data. By adjusting these parameters during training and establishing their relationship, the system achieves effective matching between the two probability distributions, thereby resolving the information loss problem while maintaining high data analysis accuracy.
2Productivity
If the latent space dimensionality is reduced to improve efficiency, then processing speed increases, but the ability to preserve probability distribution information deteriorates
Solution Approach 1:
The patent changes the parameters of the probability distribution representation in the latent space. By learning and preserving the distribution parameters (mean, variance, and potentially higher-order moments) even in reduced-dimensional latent space, the system maintains probability distribution information despite dimensionality reduction, thus achieving both processing efficiency and information preservation.
Solution Approach 2:
The patent transitions from preserving raw high-dimensional data to preserving probability distribution parameters in a compressed latent space. This dimensional transformation allows the system to represent complex distribution information in fewer dimensions by capturing essential statistical characteristics rather than full data details, thereby maintaining information quality while improving processing efficiency.
Data Source
AI summary
A training method of an autoencoder that performs encoding and decoding, for a computer to execute a process includes encoding input data by the autoencoder; obtaining a probability distribution of feature data obtained by encoding the input data; generating first decoded data by decoding the feature data by the autoencoder; adding a noise to the feature data by the autoencoder; generating second decoded data by decoding the feature data to which the noise is added by the autoencoder; and training the autoencoder to train the probability distribution of the feature data so that a first error between the first decoded data and the input data, a second error between the first decoded data and the second decoded data, and an information entropy of the probability distribution are decreased.