Feature Detector

A Feature Detector is a detector of a derived predictor feature.

• Context:
  • It can be a task output to a Feature Learning Task.
  • It can range from being a Linear Feature Detector to being a Nonlinear Feature Detector.
  • …
• Counter-Example(s):
  • a Basic Predictor Feature.
• See: Hidden Neural Network Layer, Binary Feature Vector.

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References

2012

• (Dahl et al., 2012) ⇒ George E. Dahl, Dong Yu, Li Deng, and Alex Acero. (2012). “Context-Dependent Pre-trained Deep Neural Networks for Large-Vocabulary Speech Recognition.” In: IEEE Transactions on Audio, Speech, and Language Processing, 20(1). doi:10.1109/TASL.2011.2134090
  • QUOTE: Recently, a major advance has been made in training densely connected, directed belief nets with many hidden layers. The resulting deep belief nets learn a hierarchy of nonlinear feature detectors that can capture complex statistical patterns in data. …

2006

• (Hinton & Salakhutdinov, 2006) ⇒ Geoffrey E. Hinton, and Ruslan R. Salakhutdinov. (2006). “Reducing the Dimensionality of Data with Neural Networks.” In: Science, 313(5786). doi:10.1126/science.1127647
  • QUOTE: An ensemble of binary vectors (e.g., images) can be modeled using a two-layer network called a "restricted Boltzmann machine" (RBM) (5, 6) in which stochastic, binary pixels are connected to stochastic, binary feature detectors using symmetrically weighted connections. The pixels correspond to "visible" units of the RBM because their states are observed; the feature detectors correspond to "hidden" units. A joint configuration [math]\displaystyle{ (\mathbf{v}, \mathbf{h}) }[/math] of the visible and hidden units has an energy (7) given by...

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