Feature Dimension Reduction for High-Dimensional Training Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large-scale training data for machine learning algorithms, particularly in high-dimensional feature spaces, leads to increased computation difficulty and reduced efficiency, making it challenging to achieve high prediction precision and meet service update requirements, especially in applications like Internet computational advertising.
Innovation Solution
A method that determines the importance value of each feature column, performs dimension reduction or raising based on threshold conditions, and merges feature columns to reduce the number of features, thereby improving computation efficiency and model training precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large-scale training data with high-dimensional features is used to train machine learning models, then prediction precision is improved, but computation difficulty increases and computation efficiency decreases
Solution Approach 1:
The patent extracts and removes redundant features from the training data through feature selection and dimensionality reduction techniques. By identifying and eliminating features that do not contribute significantly to prediction accuracy, the system reduces the feature dimension from tens of billions to a manageable scale, thereby decreasing computation difficulty while preserving prediction precision.
Solution Approach 2:
The patent segments the massive training data into manageable chunks through distributed processing. By dividing the data processing task across multiple computing nodes and processing features in stages (including iterative dimensionality reduction), the system makes the computation tractable while maintaining model training effectiveness.
2Measurement precision
If large-scale training data with high-dimensional features is used to train machine learning models, then prediction precision is improved, but computation efficiency decreases
Solution Approach 1:
The patent extracts only the most important features through dimensionality reduction, removing redundant dimensions that consume computational resources. This extraction process reduces the feature space from tens of billions of dimensions to a compact representation, significantly improving computation efficiency while retaining prediction precision.
Solution Approach 2:
The patent dynamically adjusts the number of features (dimensionality) during the training process. By iteratively reducing the feature dimension based on importance metrics and threshold conditions, the system optimizes the balance between model accuracy and training speed, achieving high computation efficiency without sacrificing prediction precision.
3Productivity
If parallel processing is used to process training data, then computation efficiency is improved for certain data scales, but the method cannot handle data exceeding petabyte level
Solution Approach 1:
The patent transforms the problem from handling massive data volume in the original feature space to a lower-dimensional feature space. By applying dimensionality reduction techniques, the system changes the dimensionality of the feature representation, enabling processing of petabyte-scale data through distributed systems without being constrained by the original high-dimensional complexity.
Solution Approach 2:
The patent performs preliminary dimensionality reduction and feature selection before the main model training process. By pre-processing the data to reduce its dimensionality and remove redundant features, the system prepares the data in a form that can be efficiently handled by distributed computing systems, extending the computing range to petabyte-level data.
4Device complexity
If feature dimension is reduced to improve computation efficiency, then computation difficulty decreases, but training precision may be affected
Solution Approach 1:
The patent applies different processing strategies to different features based on their importance. Rather than uniformly reducing all features, the system identifies critical features that must be preserved and applies dimensionality reduction selectively to less important features. This local differentiation maintains training precision while achieving computation efficiency.
Solution Approach 2:
The patent dynamically adjusts the dimensionality reduction level based on the threshold condition of feature importance. By changing the number of retained features adaptively during iterative processing, the system optimizes the trade-off between computation difficulty and training precision, ensuring that enough information is retained to maintain model accuracy.
Data Source
AI summary
Embodiments of the present disclosure provide a method and system for training a model by using training data. The training data includes a plurality of samples, each sample includes N features, and features in the plurality of samples form N feature columns, and the method includes: determining an importance value of each of the N feature columns; determining whether the importance value of each of the N feature columns satisfies a threshold condition; performing a dimension reduction on M feature columns to generate P feature columns in response to the determination that the importance values of the M feature columns do not satisfy the threshold condition, wherein M<N and P<M; merging (N−M) feature columns having importance values that satisfy the threshold condition and the generated P feature columns to obtain (N−M+P) feature columns; and training the model based on the training data including the (N−M+P) feature columns.