Eigen Response Analysis for Cluster Count Estimation
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Solution Overview
Problem
Spectral clustering methods face challenges in accurately estimating the number of clusters, particularly in distinguishing between speaker-indicative eigenvectors and noisy eigenvectors, due to the difficulty in determining the right cutoff point for the eigengap in similarity matrices.
Innovation Solution
A processor-based system that performs eigen response analysis on eigenvectors calculated from a similarity matrix, classifying them as cluster-indicative or noise, and outputs an estimated number of clusters by analyzing the energy of eigenvectors and using a threshold-based win counter to separate signed-eigenvectors, thereby improving the calculation of cluster numbers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral clustering methods are used to estimate the number of clusters, then clustering can be performed, but the accuracy of estimating the number of clusters deteriorates due to difficulty in determining the cutoff point for the eigengap
Solution Approach 1:
The patent applies feedback by using the computed number of clusters to guide the clustering process. The eigen response analysis provides feedback about the structure of the data through the similarity matrix and eigenvectors, allowing the system to adaptively determine the number of clusters rather than relying on a fixed or difficult-to-determine cutoff point.
Solution Approach 2:
The patent changes the parameter approach by transforming the problem from directly determining a cutoff point in the eigengap to analyzing the responses of eigenvectors to the similarity matrix. This parameter transformation simplifies the detection process and improves accuracy in estimating the number of clusters.
2Measurement precision
If eigen response analysis is performed on eigenvectors to classify them as cluster-indicative or noise, then the number of clusters can be accurately estimated, but the computational complexity increases
Solution Approach 1:
The patent extracts the essential information needed for cluster identification from the eigenvector analysis. By computing the response of each eigenvector to the similarity matrix and selecting only the top responding eigenvectors, the system extracts the most significant cluster information while filtering out noise, thus reducing unnecessary computational complexity.
Solution Approach 2:
The patent applies partial action by analyzing only the top k eigenvectors with the highest responses rather than all eigenvectors. This selective approach maintains accuracy in estimating the number of clusters while significantly reducing computational complexity compared to analyzing the complete eigenvector set.
Data Source
AI summary
An example system includes a processor to receive a data set and similarity scores. The processor is to execute an eigen response analysis on eigenvectors calculated for a similarity matrix generated based on the similarity scores for the data set. The processor is to output an estimated number of clusters in the data set based on the eigen response analysis.