Neural Network Fixed Classification Matrix Reducing Parameter Complexity
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Solution Overview
Problem
Neural networks face increased complexity and resource requirements due to the large number of parameters needed for classification, especially as the number of classes grows, leading to high storage and computational demands.
Innovation Solution
A neural network architecture that computes a D-dimensional query vector and generates a set of C fixed D-dimensional quasi-orthogonal bipolar vectors as a classification matrix, allowing for efficient classification using a fixed classification matrix that remains unchanged during training, reducing memory and computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a learned affine transformation is used for classification, then classification accuracy is improved, but the number of parameters grows linearly with the number of classes, increasing storage and computational resources
Solution Approach 1:
The patent extracts only the essential classification information by replacing the full affine transformation matrix with a compact set of C bipolar vectors. These vectors are extracted from the original parameter space and used to represent class relationships, significantly reducing the number of parameters while maintaining classification capability.
Solution Approach 2:
The patent changes the parameter representation from continuous affine transformation weights to discrete bipolar vectors with values in {-1, +1}. This parameter transformation reduces memory requirements and enables more efficient computation while preserving the essential classification functionality.
2Adaptability or versatility
If the number of classes increases, then classification versatility is improved, but the number of parameters increases linearly, requiring increasingly more resources
Solution Approach 1:
The bipolar vectors serve multiple functions: they encode class identities, represent class relationships, and enable classification computation. This multi-functionality allows the system to handle an arbitrary number of classes without proportionally increasing resource requirements, as the same vector structure adapts to different class counts.
Solution Approach 2:
By changing parameters to bipolar format, the storage requirement becomes proportional to the dimensionality D rather than growing linearly with the number of classes C in traditional approaches. This parameter transformation enables the system to scale to larger numbers of classes with controlled resource usage.
3Device complexity
If a fixed classification matrix is used, then computational complexity is reduced, but adaptability to different classification tasks may be limited
Solution Approach 1:
The bipolar vectors are pre-computed and stored in a fixed classification matrix before actual classification tasks. This preliminary action enables efficient computation during inference, as the matrix structure is established in advance and can be reused across different classification scenarios without re computation.
Solution Approach 2:
The fixed classification matrix with bipolar vectors serves as a universal classifier that can be applied to different classification tasks. The matrix structure is designed to be task-agnostic, allowing it to adapt to various classification problems while maintaining its fixed, computationally efficient form.
Data Source
AI summary
A computer-implemented method for performing a classification of an input signal by a neural network includes: computing, by a feature extraction unit of the neural network, a D-dimensional query vector, wherein D is an integer; generating, by a classification unit of the neural network, a set of C fixed D-dimensional quasi-orthogonal bipolar vectors as a fixed classification matrix, wherein C is an integer corresponding to a number of classes of the classification unit; and performing a classification of a query vector based, at least in part, on the fixed classification matrix.