Distributed Gaussian Process Classification via Matrix Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Gaussian process classification with Laplacian approximation is computationally inefficient for large datasets, making it impractical for datasets with more than a million observations due to excessive computing time.
Innovation Solution
A distributed classification model is implemented, where computation requests are sent to multiple worker devices, each computing a second-order derivative of a likelihood function on subsets of training data, and the results are concatenated to form a global matrix, allowing for efficient computation of classification probabilities across the entire dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Gaussian process classification with Laplacian approximation is used, then classification accuracy is improved, but computation time increases prohibitively for large datasets
Solution Approach 1:
The patent divides the large training dataset into multiple subsets and distributes them across multiple worker computing devices. Each worker independently computes the second order derivative matrix for its subset, enabling parallel processing. This segmentation allows the system to handle large datasets by breaking them into manageable chunks that can be processed simultaneously, resolving the contradiction between maintaining classification accuracy and reducing computation time.
2Quantity of substance
If traditional Gaussian process classification is applied to datasets with more than a million observations, then comprehensive data analysis is achieved, but the method becomes unworkable due to excessive computing time
Solution Approach 1:
The patent transitions from single-machine sequential processing to multi-machine parallel processing by adding the dimension of distributed computing. Worker computing devices are distributed across multiple nodes, each handling a portion of the data in parallel. This dimensional shift enables the system to process large datasets efficiently by leveraging the computational power of multiple devices simultaneously, making datasets with more than a million observations tractable.
Data Source
AI summary
A computing system classifies distributed data. A first computation request is sent to worker computing devices. A first response is received from each worker computing device. Each first response includes a first matrix computed as a second order derivative of a logarithm of a predefined likelihood function on a subset of training data distributed to each respective worker computing device. A global first matrix is defined by concatenating the first matrix from each worker computing device. A kernel matrix is computed using the training data and a predefined kernel function. A second computation request is sent to the worker computing devices. The second computation request indicates that each worker computing device compute a classification probability for each observation vector distributed to a respective worker computing device using the defined global first matrix and the computed kernel matrix. The determined classification probability is output for each observation vector.