Time-Series Embeddings Using Contrastive Learning for Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge of translating high-dimensional time-series data into lower-dimensional embeddings is exacerbated by non-linear relationships, making it difficult to identify relevant data links and classify electrical activity or behavioral patterns effectively.
Innovation Solution
A method involving contrastive learning is employed to select positive and negative samples from time-series data, mapping them into a common embedding space using deep neural networks, and optimizing the embedding space through a contrastive loss function to retain relevant information while reducing dimensionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional dimensionality reduction methods are used on high-dimensional time-series data with non-linear relationships, then the data can be reduced to lower dimensions, but the relevant information links are lost or obscured making classification difficult
Solution Approach 1:
The patent introduces an intermediary embedding space that acts as a mediator between the high-dimensional input space and the classification task. This embedding space preserves relevant information links through carefully constructed positive and negative sample pairs, allowing the system to maintain information integrity while reducing dimensionality for effective classification.
2Measurement precision
If more dimensions are retained in the embedding space to preserve non-linear relationships, then classification accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent changes the parameters of the embedding space by optimizing the dimensionality and the distribution of positive and negative samples. By adjusting these parameters, the system achieves a balance where the embedding space is sufficiently rich to capture non-linear relationships for accurate classification while remaining compact enough to enable efficient processing and maintain high productivity.
3Manufacturing precision
If the embedding space is optimized to separate dissimilar samples, then classification discrimination improves, but the risk of over-separating similarly labeled data increases
Solution Approach 1:
The patent implements feedback mechanisms through the contrastive learning objective function that continuously adjusts the embedding space based on the performance with respect to both positive and negative samples. This feedback loop ensures that the embedding space optimizes discrimination of dissimilar samples while maintaining appropriate separation for similarly labeled data, balancing discrimination precision with reliability of similarity preservation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for dimensionality reduction of time-series using contrastive learning. A method can include receiving multidimensional input time series data that includes data from a session or sessions that span time, selecting positive samples and negative samples from the multidimensional input time series data for respective reference samples from the multidimensional input time series data, wherein the positive samples and negative samples are each selected with a respective predetermined distribution across the time of the session or sessions of the multidimensional input time series, and mapping the reference samples, the positive samples, and negative samples into a common embedding space.