Online Heterogeneous Mixture Learning for Big Data
arXiv:1906.08068v1
AI Analysis
This work addresses the problem of efficient big data analysis for applications requiring real-time processing, though it appears incremental in nature.
The paper tackles the challenge of learning from heterogeneous big data in an online setting, achieving convergence speed comparable to batch methods with similar accuracy.
We propose the online machine learning for big data analysis with heterogeneity. We performed an experiment to compare the accuracy of each iteration between batch one and online one. It is possible to converge quickly with the same accuracy as the batch one.