Probabilistic Matrix Factorization for Automated Machine Learning Workflow Selection
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Solution Overview
Problem
Conventional machine learning approaches require extensive manual effort and are inefficient due to the vast number of preprocessing procedures, algorithms, and parameter settings, often resulting in sub-optimal workflows when applied to real-world problems.
Innovation Solution
The implementation of probabilistic matrix factorization for automated machine learning, which generates a matrix of workflows and data sets, selects and trains workflows, records performance metrics, and uses low-rank decomposition to predict the performance of workflows on new data sets, allowing for the identification of an optimum workflow for generating predicted outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional machine learning approaches are used with multiple preprocessing procedures, algorithms, and parameter settings, then the ability to handle real-world problems is improved, but the complexity of the workflow and time required increase significantly
Solution Approach 1:
The patent transforms the machine learning workflow selection problem into a parameter optimization problem by representing different workflows as parameter configurations in a matrix factorization model. The system learns optimal parameter settings (workflow configurations) by factorizing the performance matrix into latent factors, automatically identifying the best preprocessing procedures, algorithms, and parameters for given data characteristics without manual enumeration of all possibilities.
Solution Approach 2:
The patent introduces a matrix factorization model as an intermediary between the vast space of possible workflows and the specific data problem. This model acts as a mediator that maps data characteristics to optimal workflow configurations through learned latent representations, eliminating the need for direct exploration of all possible workflow combinations by experienced data scientists.
2Measurement precision
If extensive testing of multiple workflows on multiple data sets is performed, then the accuracy of workflow selection is improved, but the computational time and cost become unmanageable
Solution Approach 1:
The patent performs preliminary action by pre-factorizing the workflow performance matrix into latent factors during an offline training phase. This pre-computed factorization captures the essential relationships between workflows and data sets, enabling rapid prediction of optimal workflows for new data sets without performing extensive online testing. The system learns from a subset of data in advance and applies this knowledge efficiently to new problems.
Solution Approach 2:
The patent applies partial action by using matrix factorization on a subset of available data sets and workflows to learn the underlying performance patterns. Rather than exhaustively testing all possible workflow-data set combinations, the system performs factorization on a representative subset, which is sufficient to capture the essential relationships and enable accurate predictions for unseen data sets through the learned latent space.
3Manufacturing precision
If manual selection and fine-tuning of machine learning workflows is performed by experienced data scientists, then the quality of workflow optimization is improved, but the automation level and scalability decrease
Solution Approach 1:
The patent implements self-service by enabling the system to automatically select and optimize workflows without human intervention. The matrix factorization model autonomously learns the relationships between data characteristics and workflow performance, then automatically predicts the optimal workflow configuration for new data sets. This eliminates the need for experienced data scientists to manually evaluate and select workflows, achieving both high automation and maintained optimization quality.
Solution Approach 2:
The patent substitutes the mechanical process of manual workflow selection and fine-tuning by data scientists with an automated computational system based on matrix factorization. The manual expert judgment process is replaced by a mathematical model that learns from data and automatically makes workflow recommendations, scaling the expertise of experienced scientists to handle many more problems simultaneously without proportional increases in human resources.
Data Source
AI summary
In embodiments of probabilistic matrix factorization for automated machine learning, a computing system memory maintains different workflows that each include preprocessing steps for a machine learning model, the machine learning model, and one or more parameters for the machine learning model. The computing system memory additionally maintains different data sets, upon which the different workflows can be trained and tested. A matrix is generated from the different workflows and different data sets, where cells of the matrix are populated with performance metrics that each indicate a measure of performance for a workflow applied to a data set. A low-rank decomposition of the matrix with populated performance metrics is then determined. Based on the low-rank decomposition, an optimum workflow for a new data set can be determined. The optimum workflow can be one of the different workflows or a hybrid of at least two of the different workflows.