Incremental Slow Feature Analysis
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1354-1358.
Abstract
The Slow Feature Analysis (SFA) unsupervised learning framework extracts features representing the underlying causes of the changes within a temporally coherent high-dimensional raw sensory input signal. We develop the first online version of SFA, via a combination of incremental Principal Components Analysis and Minor Components Analysis. Unlike standard batch-based SFA, online SFA adapts along with non-stationary environments, which makes it a generally useful unsupervised preprocessor for autonomous learning agents. We compare online SFA to batch SFA in several experiments and show that it indeed learns without a teacher to encode the input stream by informative slow features representing meaningful abstract environmental properties. We extend online SFA to deep networks in hierarchical fashion, and use them to successfully extract abstract object position information from high-dimensional video.
@inproceedings{ijcai2011p229,
title = {Incremental Slow Feature Analysis},
author = {Kompella, Varun Raj and Luciw, Matthew D. and Schmidhuber, Jürgen},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1354--1358},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-229},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-229},
}
TY - CPAPER TI - Incremental Slow Feature Analysis AU - Kompella, Varun Raj AU - Luciw, Matthew D. AU - Schmidhuber, Jürgen T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1354 EP - 1358 DO - 10.5591/978-1-57735-516-8/IJCAI11-229 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-229 ER -Copied
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