Tabular Neural Network for Dataset Dimensionality Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Artificial intelligence models, particularly those trained on time-series data, face challenges due to the need for extensive feature engineering, which can lead to high dimensionality and computational expense.
Innovation Solution
The use of a tabular neural network to extract non-linear transformations of features, minimizing the dimensionality of the original dataset by generating synthetic features and embeddings, thereby reducing the complexity of feature engineering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive feature engineering is performed to improve model performance, then the model can capture more complex patterns, but the dimensionality of the dataset increases exponentially leading to computational expense
Solution Approach 1:
The patent extracts only the essential information from the original high-dimensional data by using a neural network to learn compressed representations (embeddings) that capture the most important patterns. This extraction process reduces dimensionality while preserving the necessary information for accurate predictions, avoiding the need to process all original high-dimensional features.
Solution Approach 2:
The patent changes the representation parameters of the data by transforming the original features into a lower-dimensional embedding space through neural network layers. This parameter transformation consolidates multiple high-dimensional features into fewer, more meaningful dimensions that maintain predictive power while reducing computational complexity.
2Reliability
If the number of features is increased to capture more patterns, then the model can improve accuracy, but the computational cost and processing time increase significantly
Solution Approach 1:
The system extracts and learns only the most informative patterns from the data by training a neural network to map input features to a compressed embedding representation. This extraction process filters out redundant information and retains only the essential patterns needed for accurate predictions, thereby reducing the computational burden while maintaining accuracy.
Solution Approach 2:
The patent introduces a new dimensional representation (embedding space) that consolidates information from the original high-dimensional feature space. By transforming data into this lower-dimensional latent space, the system achieves better computational efficiency while preserving the predictive accuracy that would otherwise require extensive high-dimensional features.
3Ease of operation
If traditional feature engineering methods are used, then the process is transparent and controllable, but it requires manual intervention and is time-consuming
Solution Approach 1:
The neural network automatically performs feature engineering by learning optimal transformations and representations from the data itself, without requiring manual intervention. The model self-adjusts to identify the most informative patterns and creates meaningful features through its internal learning process, significantly reducing the time and manual effort traditionally required for feature engineering.
Solution Approach 2:
The system performs feature learning and transformation as a preliminary step during the training process itself, rather than requiring separate manual feature engineering phases. By embedding feature extraction within the training workflow, the system eliminates time-consuming pre-processing steps and provides transparent, automated feature creation that is controllable through model architecture design.
Data Source
AI summary
Systems and methods for minimizing dimensionality of a high-dimensionality dataset during feature engineering. The system achieves this by using a tabular neural network to extract non-linear transformations of features without dramatically increasing the dimensionality of the original dataset. The system receives an original dataset for classification and a defined number of final features (e.g., dimensionality) that result from the synthetic feature creation and the neural network embedding process. Once an architecture of a model is determined, a model is fit on a synthetic feature set (e.g., a second dataset comprising synthetic features) with a given classification as a target.