Classifier Evaluation by Sum Estimation on Partitioned Vector Corpora
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Solution Overview
Problem
Training an image classification engine is cumbersome and resource-intensive, often requiring manual annotation of thousands of examples, which is costly and inefficient.
Innovation Solution
A sum estimation technique using strategically selected sampling and partitioning of a corpus of vectors, combined with HNSW indexing, to efficiently evaluate classifier performance by estimating distribution of classification confidences, allowing for computationally efficient evaluation without the need for large manually annotated test sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of thousands of examples is used to train image classification engine, then classification accuracy is improved, but training time and resource consumption increase
Solution Approach 1:
The patent applies partial action by using a small subset of the corpus (e.g., top-K nearest neighbors or random samples) to estimate classifier performance instead of processing the entire corpus. This allows rapid evaluation while maintaining sufficient accuracy for training decisions, significantly reducing training time without requiring full corpus annotation.
Solution Approach 2:
The patent performs preliminary action by pre-computing and storing the corpus in a vector database with indexing structures (e.g., HNSW). This preprocessing enables fast nearest neighbor searches during classifier evaluation, making subsequent assessments computationally efficient without requiring full re-processing of the entire corpus.
2Measurement precision
If manual annotation of thousands of examples is used to train image classification engine, then classification accuracy is improved, but computational resources increase
Solution Approach 1:
The patent uses partial action by evaluating classifier performance on a small sampled subset rather than the entire corpus. This dramatically reduces computational resource requirements while providing sufficient feedback for training decisions, making the process feasible on devices with limited resources.
Solution Approach 2:
The patent substitutes traditional mechanical computation (processing all examples) with a probabilistic estimation approach using sampling and statistical methods. This replacement reduces computational complexity from linear in corpus size to constant or logarithmic time operations, significantly lowering energy consumption.
3Measurement precision
If large manually annotated test sets are used to evaluate classifier, then evaluation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent applies self-service by using the existing corpus data structure (already stored in vector database with indexing) to perform evaluation without requiring separate test set infrastructure. The same corpus serves both training and evaluation purposes through different sampling strategies, simplifying the overall system architecture.
Solution Approach 2:
The patent makes the corpus multi-functional by using it for both training and evaluation purposes. The vector database and indexing structures serve dual purposes: enabling efficient nearest neighbor search for training data retrieval and facilitating rapid sampling for classifier evaluation, eliminating the need for separate test set management.
Data Source
AI summary
For example, some methods for evaluation of classifier using sum estimation may include accessing at least a part of a corpus of vectors that has been partitioned into vector datasets of different sizes, wherein the corpus of vectors has an order based on an inner product of a vector from the corpus of vectors with a vector of classifier parameters; forming a sample of the corpus of vectors by selecting, from at least two of the vector datasets, up to K vectors that are closest to an end of the order within their respective vector dataset, wherein K is an integer; identifying vectors in the sample satisfying a condition; and determining an estimate of a count of vectors satisfying the condition in the corpus of vectors based on a weighted count of the identified vectors in the sample.