Matrix Sparsity Evaluation Method for Machine Learning
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Solution Overview
Problem
Existing methods for evaluating matrix sparsity in machine learning are ineffective, as they fail to consider the distribution of non-zero elements and the overall zero tendency of the matrix, leading to increased learning difficulty and reduced model performance.
Innovation Solution
A data processing method that generates first evaluation data representing the dispersion of eigenvalues in terms of distribution and second evaluation data representing the degree of eigenvalues approaching zero, which are then used to perform a comprehensive sparsity evaluation of the matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sparsity evaluation methods are used that only count zero elements, then the evaluation process is simple, but the evaluation accuracy is low and does not reflect the true learning difficulty
Solution Approach 1:
The patent segments the sparsity evaluation into two distinct components: first evaluation data measuring the proportion of zero elements, and second evaluation data measuring the dispersion of non-zero elements. This segmentation allows each component to be calculated independently using different formulas, improving overall evaluation accuracy while maintaining computational efficiency through modular processing.
Solution Approach 2:
The patent transitions from a single-dimension sparsity metric (only counting zero elements) to a two-dimension evaluation system that also considers the dispersion distribution of non-zero elements. This dimensional expansion captures the true learning difficulty by accounting for both the quantity of zeros and the spatial distribution pattern of non-zero values.
2Quantity of substance
If sparse matrices with high zero-element proportion are used for training, then data storage efficiency is improved, but model learning difficulty increases and performance decreases
Solution Approach 1:
The patent performs preliminary evaluation of matrix sparsity characteristics before model training by calculating both the zero-element proportion and non-zero element dispersion. This preliminary assessment allows practitioners to identify matrices that will create learning difficulties in advance, enabling them to take corrective actions such as data transformation or feature engineering before training begins, thereby preventing poor model performance.
Solution Approach 2:
The patent establishes a feedback mechanism where the sparsity evaluation results (combining zero proportion and dispersion metrics) inform subsequent decisions about data processing and model training strategies. This feedback loop allows continuous optimization of the training process based on measured sparsity characteristics, improving model performance while maintaining storage efficiency.
Data Source
AI summary
An embodiment of the present application provides a data processing method, apparatus, and device, where the method includes: obtaining a to-be-processed target matrix, generating first evaluation data of the target matrix according to position information of the eigenvalue in the target matrix and the eigenvalue, the eigenvalue included in the target matrix, generating second evaluation data of the target matrix according to eigenvalue constraint information and the eigenvalue included in the target matrix, performing a sparsity evaluation on the target matrix according to the first evaluation data and the second evaluation data. Through the embodiment of the present application, an accuracy of a matrix sparsity evaluation can be improved.