Ensemble Visualization Embeddings for Large-Scale Data Exploration
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Solution Overview
Problem
Existing data visualization algorithms for large datasets face challenges in harmonizing diverse algorithmic visualizations due to high memory and run-time complexity, inability to capture non-linear relationships, and require extensive computational resources for selecting suitable algorithms.
Innovation Solution
A method involving a random forest classifier to generate an ensemble visualization graph by shuffling data, extracting embedding vectors, and generating an embedding matrix to harmonize algorithmic visualizations, reducing memory consumption and capturing non-linear dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to harmonize multiple algorithmic visualizations, then comprehensive data exploration is achieved, but memory complexity and run-time complexity become excessively high
Solution Approach 1:
The patent segments the harmonization process by dividing multiple algorithmic visualizations into individual visualization matrices, then processing them through separate computational steps (shuffling, matrix multiplication, embedding extraction) before combining results. This segmentation reduces the memory burden of handling all visualizations simultaneously while maintaining comprehensive analysis capability.
Solution Approach 2:
The patent introduces an intermediary random forest classifier that processes visualization matrices through embedding extraction. This intermediary component transforms complex visualization data into simplified embedding vectors, reducing computational complexity while preserving essential patterns for comprehensive data exploration.
2Device complexity
If linear methods are used to combine visualizations, then computational simplicity is maintained, but non-linear relationships among visualizations cannot be captured
Solution Approach 1:
The patent changes the parameter representation by transforming visualization matrices into embedding vectors through random forest classification. This parameter transformation enables the capture of non-linear relationships while maintaining computational efficiency, as the embedding space naturally represents complex patterns without requiring explicit non-linear combination operations.
Solution Approach 2:
The patent replaces traditional mechanical linear combination methods (matrix addition, weighted averaging) with a statistical learning approach using random forest classifiers. This substitution enables automatic capture of non-linear relationships through the inherent decision tree structure, which naturally models complex interactions without requiring explicit non-linear operation design.
3Measurement precision
If extensive computational resources are allocated to select suitable algorithms, then visualization accuracy is improved, but run-time complexity increases significantly
Solution Approach 1:
The patent implements self-service by allowing the random forest classifier to automatically determine the optimal harmonization strategy through embedding extraction. The system autonomously processes visualization matrices and identifies patterns without requiring external algorithm selection or manual configuration, thereby maintaining high accuracy while reducing runtime overhead associated with algorithm selection procedures.
Data Source
AI summary
Method, system, and computer-readable storage media for generating a data visualization. A plurality of algorithmic visualizations is received for a dataset. Based on the plurality of visualization matrices, a matrix is generated. Each visualization matrix of the plurality of visualization matrices corresponds with an algorithmic visualization of the plurality of algorithmic visualizations. A synthetic matrix is generated by randomly shuffling a plurality of values in each column of the matrix. A random forest classifier is trained to generate a random forest for distinguishing shuffled data of the synthetic matrix from unshuffled data of the matrix. Further, an ensemble visualization graph is generated using the random forest. From the ensemble visualization graph, an embedding vector corresponding to each sample of the dataset is extracted to generate an embedding matrix. Based upon the embedding matrix, the method includes generating the data visualization by harmonizing the plurality of algorithmic visualizations.