Gaussian Mixture Model Normalization for Neural Network Training
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Solution Overview
Problem
Machine learning systems face challenges with non-normal data distributions, leading to underfitting or overfitting, and require multiple models for each domain, resulting in high computational load and inefficiencies due to serial RNN architectures.
Innovation Solution
The system employs a Gaussian Mixture Model to approximate non-normal distributions by identifying clusters of normal distributions, normalizes data points within these clusters, and uses a single LSTM model or parallel neural network architecture to cover multiple domains, reducing the need for multiple models and improving training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional normalization methods are used on non-normal data distributions, then the normalization process is simple, but prediction accuracy deteriorates due to underfitting or overfitting
Solution Approach 1:
The patent segments the non-normal data distribution into multiple clusters, where each cluster is approximated by a normal distribution. This segmentation allows the system to handle complex non-normal distributions by breaking them down into manageable normal sub-distributions, thereby improving prediction accuracy while maintaining model reliability.
Solution Approach 2:
The patent employs a Gaussian Mixture Model that combines multiple normal distributions to approximate a non-normal distribution. This composite approach creates a more accurate representation of the underlying data distribution, resolving the contradiction between simple normalization and accurate prediction by synthesizing multiple simple models into a composite solution.
2Measurement precision
If separate machine learning models are trained for each domain of input data, then domain-specific accuracy is improved, but computational load and memory requirements increase
Solution Approach 1:
The patent trains a single machine learning model that can handle multiple domains of input data simultaneously, rather than training separate models for each domain. This universal model approach reduces computational load and memory requirements while maintaining the ability to make accurate predictions across different domains through the use of domain identification and adaptive processing.
Solution Approach 2:
The patent introduces a domain identification component that acts as an intermediary between the input data and the machine learning model. This intermediary identifies the domain of incoming data and routes it appropriately, allowing a single model to effectively handle multiple domains without requiring separate specialized models for each.
3Ease of manufacture
If serial RNN architectures are used for training machine learning models, then model training is straightforward, but performance delays and inefficiencies occur
Solution Approach 1:
The patent transitions from static serial RNN processing to dynamic parallel processing by implementing a system that can simultaneously process multiple sequences and domains. This dynamic approach allows the model to adapt its processing mode based on the nature of the input data, improving training efficiency while maintaining the benefits of RNN architectures through selective parallelization where applicable.
Data Source
AI summary
The disclosure relates to systems and methods of generating a mixture model for approximating non-normal distributions of time series data. The mixture model may include clusters of normal distributions that together approximate a non-normal distribution. The mixture model may be used to normalize input data for machine learning models. For example, a machine learning model such as an autoencoder may be trained to make predictions on the normalized input data. The predictions may relate to the time series of data. In one example, the time series of data may be market data for a security. The market data my include one or more features that are normalized using the mixture model. The predictions may include a predicted rate at which a lender will charge to borrow a security for short selling, where such rate may depend on the market data for the security.