Semi-supervised Classification via Kernel Weight Decomposition
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Solution Overview
Problem
Semi-supervised learning systems face challenges in accurately classifying unlabeled data due to noisy labels and the complexity of hyperparameter selection, leading to reduced performance and increased computational costs.
Innovation Solution
A method involving the computation of a weight matrix using kernel functions applied to observation vectors, decomposition, and gradient updates to determine sparse coefficients, which are used to define a classification matrix and update labels for unlabeled data, leveraging elastic net regularization and distributed computing for scalability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If semi-supervised learning is used to classify unlabeled data, then labeling cost is reduced, but classification accuracy deteriorates due to noisy labels
Solution Approach 1:
The patent converts the harmful effect of noisy labels into a beneficial feature by using the uncertainty information from noisy labels to guide the semi-supervised learning process. The system identifies and leverages the probabilistic nature of noisy labels to improve rather than degrade classification accuracy, transforming what was previously a detrimental factor into an advantageous element for model training.
Solution Approach 2:
The patent changes the parameter representation of labels from deterministic to probabilistic. Instead of treating labels as fixed values, the system represents them as probability distributions, allowing the model to account for label uncertainty. This parameter transformation enables the system to handle noisy labels effectively while maintaining classification accuracy.
2Measurement precision
If traditional hyperparameter selection methods are used, then model performance can be optimized, but computational cost and time increase significantly
Solution Approach 1:
The patent implements self-service by enabling the system to automatically select and optimize hyperparameters without requiring extensive manual intervention or trial-and-error experimentation. The automated hyperparameter selection mechanism allows the system to configure itself optimally, reducing both the time and expertise required for model deployment while maintaining high performance.
Solution Approach 2:
The patent performs preliminary action by pre-configuring hyperparameter optimization strategies and selecting promising hyperparameter ranges before the actual training process. This advance preparation reduces the computational burden and time required during the model training phase, as the search space has already been narrowed and optimized based on preliminary analysis.
3Measurement precision
If numerous candidate models are trained to evaluate hyperparameters, then model quality improves, but computing resources and time are exhausted
Solution Approach 1:
The patent applies partial action by training a selective subset of candidate models rather than exhaustively evaluating all possible hyperparameter combinations. The system identifies and focuses computational resources on the most promising candidates based on preliminary assessments, achieving high model quality without the need to train every possible configuration, thus conserving computing resources.
Solution Approach 2:
The patent segments the hyperparameter search space into multiple independent regions or clusters, allowing parallel evaluation of different model candidates. This segmentation enables efficient utilization of computing resources by distributing the training workload across multiple processors or nodes, reducing the overall energy consumption and time required to evaluate numerous candidate models.
Data Source
AI summary
Data is classified using semi-supervised data. A weight matrix is computed using a kernel function applied to observation vectors. A decomposition of the computed weight matrix is performed. A predefined number of eigenvectors is selected from the decomposed weight matrix to define a decomposition matrix. (A) A gradient value is computed as a function of the defined decomposition matrix, sparse coefficients, and a label vector. (B) A value of each coefficient of the sparse coefficients is updated based on the gradient value. (A) and (B) are repeated until a convergence parameter value indicates the sparse coefficients have converged. A classification matrix is defined using the converged sparse coefficients. The target variable value is determined and output for each observation vector based on the defined classification matrix to update the label vector and defined to represent the label for a respective unclassified observation vector.