4.4 Performance Considerations
Feature extraction can be computationally intensive and difficult to scale. Optimization strategies have been considered to ensure that feature extraction is efficient and scalable.
- Parallel Processing: Parallel & distributed feature extraction for independent features.
---
title: "Figure 4.1: Distributed Parallel Feature Extractors"
---
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graph TD
A[Record] --> B[Feature Extractor A]
B --> C[Extract Feature A]
A --> D[Feature Extractor B]
D --> E[Extract Feature B]
A --> F[Feature Extractor C]
F --> G[Extract Feature C]
C --> H[Combine Features]
E --> H
G --> H
H --> I[Feature Set]
-
Caching: Caching of intermediate results for frequently accessed data.
-
Optimized Data Structures: Optimise schemas to allow efficient data structures for feature representation and manipulation.
-
Batched Processing: Process multiple records in batches to amortize overhead costs.
-
Feature Selection: Features selected carefully to provide the most discriminative power for downstream tasks.
Performance can be quantified using the following metrics:
- Extraction Throughput
- Feature Extraction Latency
- Memory Efficiency