Dimensionality Reduction in Prediction Model Training
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Solution Overview
Problem
Training complex prediction models in high-dimensional feature spaces is inefficient, particularly for non-linear models, due to sparse feature vectors and high dimensionality.
Innovation Solution
A training system that reduces dimensionality by partitioning aspect values and generating statistical information for subsets, treating these instances as features to train the model, thereby minimizing loss of predictive accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate features are allocated to individual aspect values in high-dimensional feature space, then the prediction model can capture detailed information, but the training efficiency deteriorates and the feature space becomes too sparse
Solution Approach 1:
The patent merges multiple individual aspect values into grouped aspect values. Instead of creating separate features for each individual aspect value (which creates high dimensionality and sparsity), the invention groups related aspect values together and creates features based on these groups. This combining approach reduces the total number of features while preserving the predictive information through aggregated statistical properties.
Solution Approach 2:
The patent transforms the feature space from individual aspect value dimensions to grouped aspect value dimensions. By organizing features based on groups of aspect values rather than individual aspect values, the invention changes the dimensional structure of the feature space, reducing sparsity and improving training efficiency while maintaining predictive accuracy through the grouped statistical representations.
2Measurement precision
If the prediction model is made complex (e.g., non-linear) to improve predictive accuracy, then the model sophistication increases, but the training difficulty and computational cost increase significantly in high-dimensional spaces
Solution Approach 1:
The patent combines multiple individual aspect values into grouped aspect values, which reduces the dimensionality of the feature space. This dimensionality reduction makes it feasible to train complex non-linear prediction models because the computational complexity of training such models grows with the square or cube of the number of features. By merging aspect values into groups, the invention enables training of sophisticated models without prohibitive computational costs.
3Loss of information
If separate features are created for each combination of attribute values, then the feature representation becomes more detailed, but the feature vector sparsity increases and dimensionality explodes
Solution Approach 1:
The patent merges individual aspect values into grouped aspect values, creating a intermediate level of abstraction between individual values and complete combinations. This grouping strategy preserves important statistical information about relationships among aspect values while avoiding the exponential dimensionality explosion that would result from creating features for every possible combination of attribute values.
Solution Approach 2:
The grouped aspect values serve multiple functions: they capture relationships among individual aspect values, reduce dimensionality, and provide a scalable feature representation that works for both simple and complex prediction tasks. This multi-functional design allows the same grouped structure to handle various prediction scenarios without requiring separate feature engineering for each case.
Data Source
AI summary
A training system is described herein for generating a prediction model that relies on a feature space with reduced dimensionality. The training system performs this task by producing partitions, each of which corresponds to a subset of aspect values (where each aspect value, in turn, may correspond to one or more attribute values). The training system then produces instances of statistical information associated with the partitions. Each instance of statistical information therefore corresponds to feature information that applies to a plurality of aspect values, rather than a single aspect value. The training system then trains the prediction model based on the feature information. Also described herein is a prediction module that uses the prediction model to make predictions in various online contexts.