Journal
NEURAL COMPUTATION
Volume 22, Issue 9, Pages 2308-2333Publisher
MIT PRESS
DOI: 10.1162/NECO_a_00010
Keywords
-
Funding
- Academy of Finland [111786]
- Centre-of-Excellence in Algorithmic Data Analysis (Algodan)
- Academy of Finland (AKA) [111786, 111786] Funding Source: Academy of Finland (AKA)
Ask authors/readers for more resources
We consider a hierarchical two-layer model of natural signals in which both layers are learned from the data. Estimation is accomplished by score matching, a recently proposed estimation principle for energy-based models. If the first-layer outputs are squared and the second-layer weights are constrained to be nonnegative, the model learns responses similar to complex cells in primary visual cortex from natural images. The second layer pools a small number of features with similar orientation and frequency, but differing in spatial phase. For speech data, we obtain analogous results. The model unifies previous extensions to independent component analysis such as subspace and topographic models and provides new evidence that localized, oriented, phase-invariant features reflect the statistical properties of natural image patches.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available