Neural Network Time Series Classification via Triplet Loss
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Solution Overview
Problem
Conventional systems face challenges in accurately classifying time series data due to the volume and complexity of the data, especially when sufficient training data is not available, leading to errors and inefficiencies in manual processing and machine learning approaches.
Innovation Solution
A processor-implemented method and system for training a neural network using iterative triplet loss-based approaches, sampling, and consolidating parameters to perform time series data classification, which includes collecting time series classification tasks, training the neural network with initial parameters, and fine-tuning using a pre-defined number of tasks to achieve accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional machine learning approaches are used to automate time series data classification, then the classification task can be automated, but the accuracy is insufficient when the amount of training data is minimal
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on source domain data before adapting it to the target domain. The model performs initial learning on abundant source data, then fine-tunes on limited target data, achieving effective automation even with minimal training samples. This is evident in the multi-stage training process where the model is first trained on source domain time series data, then adapted to target domain tasks.
Solution Approach 2:
The patent uses domain adaptation as an intermediary mechanism to bridge the gap between source domain (abundant data) and target domain (minimal data). The neural network learns domain-invariant features that transfer across domains, enabling accurate classification in the target domain without requiring extensive target-specific training data.
2Measurement precision
If more training data is collected to improve classification accuracy, then the model performance can be enhanced, but the complexity and time required for data processing increases
Solution Approach 1:
The patent segments the training process into distinct phases: source domain pre-training and target domain fine-tuning. This segmentation allows the model to learn general patterns from source data without requiring extensive target domain data, thereby reducing overall data processing complexity while maintaining high accuracy.
Solution Approach 2:
The patent extracts and transfers useful features and patterns from the source domain to the target domain. By identifying and transferring domain-invariant characteristics, the model achieves accurate classification without needing to process large amounts of target domain data, thus reducing processing complexity.
3Adaptability or versatility
If the neural network is trained on multiple tasks with varying numbers of classes, then the model's adaptability improves, but the training process becomes more complex
Solution Approach 1:
The patent implements a universal neural network architecture that can handle multiple classification tasks with varying numbers of classes. The model learns task-agnostic representations that can be adapted to any number of classes through fine-tuning, making it versatile across different time series classification problems without requiring task-specific architecture modifications.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the number of output classes and fine-tuning network parameters for each specific task. The model maintains a consistent architecture but adapts its parameters to suit different classification scenarios, enabling versatility while managing training complexity through systematic parameter adjustment.
Data Source
AI summary
Neural networks can be used for time series data classification. However, in a K-shot scenario in which sufficient training data is unavailable to train the neural network, the neural network may not produce desired results. Disclosed herein are a method and system for training a neural network for time series data classification. In this method, by processing a plurality of task specific data, a system generates a set of updated parameters, which is further used to train a neural network (network) till a triplet loss is below a threshold. The network is trained on a diverse set of few-shot tasks sampled from various domains (e.g. healthcare, activity recognition, and so on) such that it can solve a target task from another domain using only a small number of training samples from the target task.