On Sparsity and Overcompleteness in Image Models
On Sparsity and Overcompleteness in Image Models
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发表时间：
2007-12
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通讯作者：
P. Berkes;Richard E. Turner;M. Sahani
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作者：
P. Berkes;Richard E. Turner;M. Sahani
视觉皮层的计算模型，特别是基于稀疏编码的计算模型，最近备受关注。尽管有这种通用性，但稀疏表示应该有多稀疏或过度完整的问题仍然没有原则性的答案。在这里，我们使用贝叶斯模型选择方法来解决基于 Student-t 先验的稀疏编码模型的这些问题。在玩具数据上验证了我们的方法后，我们发现自然图像确实最好通过极其稀疏的分布来建模；尽管对于 Student-t 先验，相关的最佳基础大小只是适度地过度完成。
Computational models of visual cortex, and in particular those based on sparse coding, have enjoyed much recent attention. Despite this currency, the question of how sparse or how over-complete a sparse representation should be, has gone without principled answer. Here, we use Bayesian model-selection methods to address these questions for a sparse-coding model based on a Student-t prior. Having validated our methods on toy data, we find that natural images are indeed best modelled by extremely sparse distributions; although for the Student-t prior, the associated optimal basis size is only modestly over-complete.