Which technique is used to learn nonlinear dimensionality reduction by training networks to reconstruct inputs?

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Training networks to reconstruct inputs yields a compact, nonlinear representation of the data. This approach uses an autoencoder, which has an encoder that maps the input to a lower-dimensional latent code and a decoder that attempts to reconstruct the original input from that code. By minimizing reconstruction error, the model is forced to capture the essential structure of the data in the latent space. The nonlinearities in the encoder and decoder allow this representation to capture complex, curved relationships that linear methods miss. In contrast, linear techniques like PCA can only unwrap linear relationships and won’t typically model nonlinear manifolds. RNNs and CNNs are types of networks, and they can be used inside an autoencoder for specific data types, but the technique described—learning nonlinear dimensionality reduction by reconstructing inputs—is the hallmark of autoencoders.