Adaptive Representation Learning for Streaming Data with Memory Constraints

This paper presents an adaptive representation learning framework tailored for streaming data environments constrained by limited memory. The core challenge in applying traditional representation learning techniques to continuous data streams lies in the need to retain sufficient information while managing computational resources. Our approach addresses this issue through a dynamically pruning and growing representation, effectively balancing accuracy and efficiency. We introduce a mechanism where a minimal core representation is maintained, and new features are selectively added based on their impact on performance or the evolving data distribution. This adaptive strategy allows the model to continually refine its representation without incurring excessive memory overhead. The framework's effectiveness is demonstrated through theoretical analysis and conceptual design, providing a robust solution for representation learning in resource-scarce streaming data scenarios. The key innovations include the dynamic pruning and growing strategy, the feature selection criterion based on performance improvement and distribution adaptation, and the overall architecture designed for efficient memory usage. This work contributes to the field by offering a practical and scalable approach to representation learning for streaming data, addressing a critical limitation in existing methods.