Discrete Representation Learning via Combinatorial Partitioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing representation learning methods face inefficiencies in computation time and memory usage, particularly in generating discrete vector representations for objects, which are essential for tasks like recommendation and indexing.
Innovation Solution
A computer-implemented method that constructs a weighted graph preserving neighborhood semantics, uses combinatorial partitioning to generate discrete vectors, and assigns binary codes to nodes, eliminating the need for gradient descent and optimizing memory and computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing representation learning methods are used to generate discrete vector representations, then the task can be completed, but computation time and memory usage are excessive
Solution Approach 1:
The patent segments the continuous vector space into discrete partitions using combinatorial optimization. By dividing the representation space into distinct regions and assigning discrete codes to each region, the method transforms the continuous learning problem into a discrete combinatorial problem, thereby reducing computation time and memory requirements while preserving neighborhood semantics.
Solution Approach 2:
The patent changes the parameter space from continuous real-valued vectors to discrete combinatorial codes. This parameter transformation allows the system to operate in a discrete domain that requires less memory and computation time, while the combinatorial partitioning ensures that the discrete representation maintains the semantic relationships present in the continuous space.
2Productivity
If existing representation learning methods are used to generate discrete vector representations, then the task can be completed, but memory requirements are excessive
Solution Approach 1:
The patent uses lightweight discrete combinatorial codes instead of heavy continuous floating-point vectors. These discrete codes require significantly less memory storage and can be processed more efficiently, making the representation learning scalable to large datasets without excessive memory requirements.
3Loss of time
If discrete vector representations are generated efficiently, then computation time is reduced, but representation quality may deteriorate
Solution Approach 1:
The patent performs preliminary combinatorial partitioning of the vector space before generating discrete representations. By pre-defining the partition structure and code assignments, the method ensures that the discrete codes preserve neighborhood semantics and semantic relationships, maintaining representation quality while enabling efficient computation during actual use.
Data Source
AI summary
Methods, computer program products and/or systems are provided that perform the following operations: obtaining a collection of objects; constructing a weighted graph based on the collection of objections, wherein the weighted graph preserves neighborhood semantics of objects of the collection of objects; generating partitions of nodes in the weighted graph of a fixed maximum size utilizing combinatorial partitioning; generating a vector for each node based on the partitions of nodes in the weighted graph; determining vector representations for objects in this collection and eventually applying this vector representation of the objects to gain efficiency (e.g., in terms of computation time and memory requirements) for use in downstream tasks such as recommendation.