SLIDlike
SLIDlike is a class of algorithms and frameworks designed for incremental learning from streaming data. It emphasizes sparse, localized data representations that can be updated in real time while maintaining interpretability. The term SLIDlike refers to methods inspired by Sparse Localized Incremental Decomposition, though implementations vary in details and optimizations. These approaches typically maintain a compact dictionary of basis components and adapt both the components and the representation coefficients as new data arrives, without retraining from scratch.
Key ideas in SLIDlike methods include sparsity to limit the influence of any single observation, locality to
Architecture in practice often involves data ingestion and normalization, an incremental decomposition module that stores the
Applications for SLIDlike include real-time anomaly detection, adaptive recommender systems, sensor networks, and financial monitoring, especially
See also: incremental learning, online dictionary learning, sparse coding, concept drift.