Incremental Slow Feature Analysis: Adaptive Low-Complexity Slow Feature Updating from High-Dimensional Input Streams

@article{Kompella2012IncrementalSF,
  title={Incremental Slow Feature Analysis: Adaptive Low-Complexity Slow Feature Updating from High-Dimensional Input Streams},
  author={Varun Raj Kompella and Matthew D. Luciw and J{\"u}rgen Schmidhuber},
  journal={Neural Computation},
  year={2012},
  volume={24},
  pages={2994-3024}
}
We introduce here an incremental version of slow feature analysis (IncSFA), combining candid covariance-free incremental principal components analysis (CCIPCA) and covariance-free incremental minor components analysis (CIMCA). IncSFA's feature updating complexity is linear with respect to the input dimensionality, while batch SFA's (BSFA) updating complexity is cubic. IncSFA does not need to store, or even compute, any covariance matrices. The drawback to IncSFA is data efficiency: it does not… CONTINUE READING
Highly Cited
This paper has 43 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 25 extracted citations

Rejecting Motion Outliers for Efficient Crowd Anomaly Detection

IEEE Transactions on Information Forensics and Security • 2019
View 1 Excerpt

References

Publications referenced by this paper.
Showing 1-10 of 64 references

Similar Papers

Loading similar papers…