Machine Learning Data Band Selection for Efficient Model Training
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Solution Overview
Problem
Existing machine learning models often require large amounts of data to achieve high accuracy, leading to increased computational demands and power usage, with many input features contributing noise rather than predictive value.
Innovation Solution
A computer system is configured to automatically select the most appropriate data bands for a machine learning task by evaluating and scoring available data bands, allowing for the training of a subset of bands that maximizes accuracy while reducing the number of input features, thereby creating smaller, more efficient models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models use large amounts of data to achieve high accuracy, then accuracy is improved, but computational demands and power usage increase
Solution Approach 1:
The patent extracts and selects only the most important data bands from the full dataset using a machine learning model trained to identify predictive features. By removing unnecessary data bands that contribute noise rather than predictive value, the system achieves high accuracy with reduced computational demands and lower power usage.
Solution Approach 2:
The patent changes the parameter of data band selection by dynamically determining which bands are most important based on training results. The system adjusts the number and type of input features used in the machine learning model based on their predictive value, optimizing the balance between accuracy and computational efficiency.
2Measurement precision
If machine learning models use all available input features, then accuracy may be maintained, but model size and complexity increase
Solution Approach 1:
The patent extracts and removes redundant input features from the full dataset. By identifying and excluding data bands that do not contribute significant predictive value, the system reduces model size and complexity while maintaining accuracy through the use of only essential features.
Solution Approach 2:
The patent segments the full set of data bands into important and unimportant categories. The machine learning model identifies which segments (data bands) are critical for accurate predictions and uses only those, effectively dividing the feature space to reduce overall model complexity.
3Loss of information
If machine learning models use all available input features, then more information is available, but computational resources and processing time increase
Solution Approach 1:
The patent extracts only the essential information needed for accurate predictions by selecting specific data bands. By removing redundant features that do not contribute to predictive accuracy, the system maintains necessary information while significantly reducing computational resources and processing time requirements.
Solution Approach 2:
The patent applies partial action by using only a subset of available data bands rather than all features. The machine learning model determines the minimum sufficient set of features needed for accurate predictions, avoiding the excessive processing burden of analyzing all available data while maintaining adequate information for high-accuracy results.
Data Source
AI summary
Methods, systems, apparatus, and computer-readable media for data band selection using machine learning. In some implementations, image data comprising information for each of multiple wavelength bands is obtained. A multi-layer neural network is trained using the image data to perform one or more classification or regression tasks. A proper subset of the wavelength bands is selected based on parameters of a layer of the trained multi-layer neural network, where the parameters were determined through training of the multi-layer neural network using the image data. Output is provided indicating that the selected wavelength bands are selected for the one or more classification or regression tasks.