Neural Network Transformation Matrix for KNN Parameter Learning
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Solution Overview
Problem
The k-nearest neighbors algorithm requires heuristic determination of the count k of selected data and their weights, which limits its effectiveness in regression analysis and class separation.
Innovation Solution
A learning device generates a transformation matrix from learning data, allowing a neural network to automatically adjust the count k and weights of data through a first and second estimation distribution, using a Softmax layer and matrix product to optimize parameter learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the k-nearest neighbors algorithm is used with heuristic determination of count k and weights, then the method remains simple and easy to implement, but the accuracy of regression analysis and class separation is limited
Solution Approach 1:
The system performs self-learning by automatically determining the count k and weights through neural network training on learning data. The neural network learns optimal parameters from data without requiring manual heuristic determination, enabling the system to serve itself in optimizing its own parameters for accurate inference
Solution Approach 2:
The invention transforms the static heuristic parameters (count k and weights) into dynamic learnable parameters. By using a neural network with a transformation matrix, the system can automatically adjust and optimize these parameters based on learning data, changing them from fixed heuristic values to adaptive parameters that improve accuracy
2Adaptability or versatility
If manual heuristic methods are used to determine count k and weights, then the implementation process is simple, but the adaptability to different data sets is poor
Solution Approach 1:
The neural network automatically adapts to different data sets by learning from them during the training phase. When new data is provided, the system performs self-learning to determine optimal parameters specific to that data set, eliminating the need for manual heuristic adjustment and enabling automatic adaptation across different domains
Solution Approach 2:
The system performs preliminary learning during a training phase using learning data to pre-determine optimal parameters before actual inference. This preliminary action of learning from data allows the system to be rapidly adapted to different data sets without time-consuming manual parameter tuning during deployment
Data Source
AI summary
According to an embodiment, a learning device includes a memory and one or more processors coupled to the memory. The one or more processors are configured to: generate a transformation matrix from learning data in which feature quantities and target values are held in a corresponding manner; and learn about parameters of a neural network which includes nodes equal in number to the number of rows of the transformation matrix, a first output layer representing first estimation distribution according to the values of the nodes, and a second output layer representing second estimation distribution decided according to the product of the transformation matrix and the first estimation distribution.