Categorical Inference Machine Learning Engine for Predictive Data Analysis
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Solution Overview
Problem
Existing predictive data analysis solutions are inefficient and unreliable when handling categorical input data due to their design focusing on numeric inputs, leading to ineffective translation of categorical data and failure to learn from semantic structures.
Innovation Solution
The use of initial capsule layers, spatial fully-connected layers, time-distributed layers, localized convolutional layers, and regime-specific processing to generate embedded feature representations and inferred instantiation parameters for categorical data, enabling effective predictive inferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing predictive data analysis solutions are used for categorical input data, then the system can process data, but the efficiency and reliability deteriorate due to ineffective translation and failure to learn semantic structures
Solution Approach 1:
The patent introduces embedding layers as intermediary components that translate categorical input data into continuous vector representations. These embedding layers serve as a mediator between the categorical data and the neural network processing layers, enabling effective learning of semantic structures while maintaining processing efficiency and improving predictive analysis reliability
Solution Approach 2:
The patent transforms categorical data parameters into continuous vector space parameters through embedding layers. By changing the representation form from discrete categorical values to continuous vectors, the system can effectively capture semantic relationships and improve both reliability and efficiency of predictive analysis
2Loss of information
If categorical data is translated using existing methods, then processing can be performed, but the translation effectiveness deteriorates leading to loss of semantic information
Solution Approach 1:
The patent maps categorical data from a discrete low-dimensional space into a continuous high-dimensional vector space through embedding layers. This dimensional transformation allows the system to retain and even enhance semantic information by representing categorical values as vectors with meaningful geometric relationships, while the modular embedding architecture manages complexity effectively
3Reliability
If standard neural network layers are used for categorical data, then the system can process data, but training effectiveness deteriorates due to inability to learn from semantic structures
Solution Approach 1:
The patent applies preliminary action by pre-processing categorical data through embedding layers that learn semantic representations before the main training process. This preliminary transformation of categorical data into meaningful vector representations accelerates subsequent training convergence and improves training effectiveness, reducing the overall training time while enhancing model performance
Data Source
AI summary
There is a need for more effective and efficient predictive data analysis solutions that utilize categorical input data objects. This need can be addressed by, for example, solutions for performing predictive inference using a categorical inference machine learning engine. In one example, a method includes receiving categorical input data objects, generating, based on each particular categorical input data object and using embedding layers, embedded feature representations for the particular categorical input data object; generating, based on each particular embedded feature representation and using initial capsule layers; initial instantiation parameters for the corresponding categorical data object; generating, based on each initial instantiation parameter and using subsequent capsule layers, inferred instantiation parameters for categorical input data objects; and generating predictions based at least in part on the inferred instantiation parameters.