Task-Specific Feature Space Dimension Reduction for ML Models

The feature space is segmented into task-specific subspaces based on the task heads of the machine learning model. Each task head has its own feature space projection, allowing dimension reduction to be performed separately for each task-relevant subspace rather than on the entire high-dimensional space at once. This segmentation enables the dimension reduction to preserve task-specific relevant directions while maintaining efficiency.

Different regions or subspaces of the feature space are treated differently based on their task relevance. The patent applies local dimension reduction where each task head operates on its own projected feature space, preserving the specific quality and characteristics needed for each task while reducing overall dimensionality. This local approach ensures that task-critical directions are maintained.

The patent introduces a new dimensional perspective by projecting the feature space according to task heads and then performing dimension reduction in this transformed space. This dimensional transformation allows the model to capture task-relevant directions that may have low variance in the original feature space, effectively adding a new dimensional viewpoint that reveals previously hidden relevant variations.

The patent changes the parameter representation of the feature space by applying task-specific projections and dimension reduction transformations. This parameter transformation converts feature vectors from the original high-dimensional space into a reduced-dimensional task-specific space, where the parameters are reorganized to emphasize task-relevant variations while maintaining sufficient variance for effective learning.