Extracting low-dimensional psychological representations from convolutional neural networks.
Aditi JhaJoshua Caleb PetersonThomas L. GriffithsPublished in: CoRR (2020)
Keyphrases
- convolutional neural networks
- low dimensional
- high dimensional
- dimensionality reduction
- convolutional network
- high dimensional data
- manifold learning
- principal component analysis
- input space
- higher level
- pattern recognition
- linear dimensionality reduction
- automatically extracting
- nonlinear dimensionality reduction
- image processing
- feature space
- graph embedding
- multidimensional scaling
- linear subspace
- feature representation
- dimension reduction
- symbolic representation
- subject specific
- machine learning
- multiple representations
- human decision making
- bayesian networks
- feature extraction