Local Learning Multi-Task Gaussian Process for Adaptive Clustering
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Solution Overview
Problem
Existing multi-task learning approaches are too restrictive as they only exploit global relationships between tasks, neglecting local task similarities, which limits their ability to harness the full potential of adaptive multi-task learning.
Innovation Solution
The implementation of a Local Learning Multi-Task Gaussian Process (LL-MTGP) model that clusters data values into input feature clusters, trains local MTGPs for each cluster, merges data based on optimized hyper-parameters, and provides a LL-MTGP model to capture local inter-task relationships, allowing for the exploitation of local correlations between tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If global relationships between tasks are exploited in multi-task learning, then a unified model structure can be learned, but local task similarities are neglected leading to restricted adaptability
Solution Approach 1:
The patent segments the global task space into multiple local regions based on input feature clustering. Each local region has its own task similarity relationships captured by local MTGP models. This segmentation allows the system to adapt to local task similarities without requiring a single complex global model, thus improving adaptability while managing complexity through modular local models.
Solution Approach 2:
The patent applies local quality by allowing different regions of the input feature space to have different task similarity characteristics. Each local cluster is modeled with its own hyper-parameters that capture the specific inter-task relationships in that region. This enables the model to adapt locally to task similarities rather than imposing a uniform global structure, directly addressing the contradiction between adaptability and complexity.
2Measurement precision
If local inter-task relationships are captured through clustering, then hidden correlations between tasks can be identified, but computational complexity increases
Solution Approach 1:
The patent divides the data space into multiple clusters based on input features, allowing precise capture of local inter-task relationships in each cluster. By segmenting the problem into manageable local regions, the computational complexity is reduced compared to modeling all possible local relationships globally, while still achieving high precision in capturing task correlations within each local region.
Solution Approach 2:
The patent applies partial action by focusing computational resources on capturing task relationships only within local clusters rather than attempting to model all possible task interactions globally. This partial approach to modeling inter-task relationships achieves sufficient precision for local correlations while avoiding the excessive computational complexity of a complete global model.
3Adaptability or versatility
If multiple local MTGP models are trained for each input feature cluster, then local task similarities are exploited, but the number of models and hyper-parameters increases
Solution Approach 1:
The patent segments the model into multiple local MTGP instances, each responsible for a specific cluster of input features. This segmentation enables exploitation of local task similarities without requiring a single overly complex global model. The number of local models is managed by clustering algorithms that group similar instances together, reducing the effective number of independent models needed.
Solution Approach 2:
The patent applies universality by using a common MTGP framework structure across all local clusters. Each local model uses the same Gaussian process formulation and can be trained using similar algorithms, providing a universal approach that handles multiple local regions. This multi-functional framework reduces complexity by reusing the same model architecture and training procedures across different local clusters rather than requiring entirely separate models for each region.
Data Source
AI summary
Methods, systems, and computer-readable storage media for receiving a dataset, the dataset including a plurality of data values, clustering data values of the plurality of data values into a plurality of input feature clusters in input feature space, training a local multi-task Gaussian process (MTGP) for each input feature cluster to provide optimized hyper-parameters in hyper-parameter space, an optimized hyper-parameter being provided for each input feature cluster, merging data values based on the optimized hyper-parameters, and distances between hyper-parameter clusters in the hyper-parameter space to provide a plurality of merged data values, and providing a LL-MTGP model based on the merged data values.