Reducing the Dimensionality of Data with Neural Networks
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Abstract
High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such "autoencoder" networks, but this works well only if the initial weights are close to a good solution. We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.
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The record
- Venue
- Science
- Topic
- Neural Networks and Applications
- Field
- Computer Science
- Canadian institutions
- University of TorontoUniversity of New Brunswick
- Funders
- —
- Keywords
- AutoencoderCurse of dimensionalityInitializationGradient descentArtificial neural networkComputer sciencePrincipal component analysisArtificial intelligencePattern recognition (psychology)Layer (electronics)High dimensionalPrincipal (computer security)AlgorithmMaterials scienceNanotechnology
- Has abstract in OpenAlex
- yes