Autoencoder Style Vector Regularization for Time Invariant Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning techniques face challenges in classifying high-frequency time-varying input data signals, such as neurological signals, due to the difficulty in generating well-labeled training datasets and the high temporal correlation between adjacent time intervals, leading to inaccurate classifications and overfitting.
Innovation Solution
The use of a latent vector space in machine learning, comprising a label vector and a style vector, where the style vector is regularized to enforce time invariance, allowing for the generation of more reliable and robust label vectors that are less sensitive to temporal correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-frequency time-varying input data signals are sampled at high rates to retain information, then measurement precision is improved, but the volume of sequential data increases making classification more difficult and time-consuming
Solution Approach 1:
The patent segments the high-frequency time-varying input data into multiple lower-frequency representations by processing different time intervals separately. Each segment is classified independently using machine learning techniques, reducing the computational burden while preserving essential information patterns across the full frequency range.
Solution Approach 2:
The patent transforms the temporal frequency dimension by converting high-frequency time-series data into multi-dimensional feature spaces. Through techniques like wavelet transforms or principal component analysis, the data is represented in alternative dimensions that capture temporal patterns without requiring processing of every high-frequency sample.
2Reliability
If well-labelled training datasets are generated to improve classification reliability, then classification accuracy is improved, but the data labelling process becomes more difficult and time-consuming
Solution Approach 1:
The patent implements self-service through semi-supervised and unsupervised machine learning techniques that automatically learn patterns from data with minimal human labelling. The system uses unsupervised clustering to identify natural groupings in the data and semi-supervised methods to propagate labels from a small set of annotated examples to large volumes of unlabelled data, dramatically reducing manual labelling requirements.
Solution Approach 2:
The patent applies preliminary action by pre-processing the high-frequency data to extract salient features and patterns before classification. Techniques such as feature extraction, dimensionality reduction, and preliminary clustering are performed to prepare the data in advance, making the subsequent classification process more efficient and reliable without requiring extensive manual labelling of raw data.
3Stability of the object's composition
If adjacent time intervals are heavily weighted to capture temporal patterns, then temporal correlation is improved, but time invariance deteriorates leading to overfitting and reduced robustness
Solution Approach 1:
The patent applies local quality by treating different time intervals with different weighting schemes tailored to their specific characteristics. Rather than uniformly weighting all adjacent intervals, the system identifies regions of high temporal correlation and applies localized smoothing or regularization only where needed, preserving time-invariant patterns while capturing local temporal dependencies.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting regularization parameters and weighting factors based on the observed temporal patterns in the data. The system monitors temporal correlation metrics and adapts the degree of smoothing or constraint applied to different time intervals, maintaining robustness against overfitting while preserving genuine temporal relationships when present.
Data Source
AI summary
Method(s) and apparatus are provided for operating and training an autoencoder. The autoencoder outputs a latent vector of an N-dimensional latent space for classifying input data. The latent vector includes a label vector y and a style vector z. The style vector z is regularised during training for effecting time invariance in the set of label vectors y associated with the input data. Method(s) and apparatus are further provided for controlling the optimisation of an autoencoder. The autoencoder outputting a latent vector of an N-dimensional latent space for classifying input data. The latent vector comprising a label vector y and a style vector z. The regularisation of the style vector z is controlled to increase or decrease the time invariance of the label vectors y. An autoencoder configured based on the above-mentioned trained autoencoder that regularised the style vector z for effecting time invariance in the set of label vectors y associated with the input data during classification.