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Text of the page (random words):
and learned a representation that was qualitatively easier to interpret clearly separating data clusters 2 10 reducing dimensions can improve performance on tasks such as classification 2 indeed the hallmark of dimensionality reduction is to place semantically related examples near each other 39 principal component analysis edit reconstruction of 28x28pixel images by an autoencoder with a code size of two two units hidden layer and the reconstruction from the first two principal components of pca images come from the fashion mnist dataset 38 if linear activations are used or only a single sigmoid hidden layer then the optimal solution to an autoencoder is strongly related to principal component analysis pca 30 40 the weights of an autoencoder with a single hidden layer of size p displaystyle p where p displaystyle p is less than the size of the input span the same vector subspace as the one spanned by the first p displaystyle p principal components and the output of the autoencoder is an orthogonal projection onto this subspace the autoencoder weights are not equal to the principal components and are generally not orthogonal yet the principal components may be recovered from them using the singular value decomposition 41 however the potential of autoencoders resides in their non linearity allowing the model to learn more powerful generalizations compared to pca and to reconstruct the input with significantly lower information loss 10 information retrieval edit information retrieval benefits particularly from dimensionality reduction in that search can become more efficient in certain kinds of low dimensional spaces autoencoders were indeed applied to semantic hashing proposed by salakhutdinov and hinton in 2007 39 by training the algorithm to produce a low dimensional binary code all database entries could be stored in a hash table mapping binary code vectors to entries this table would then support information retrieval by returning all entries with the same binary code as the query or slightly less similar entries by flipping some bits from the query encoding anomaly detection edit another application for autoencoders is anomaly detection 17 42 43 44 45 46 by learning to replicate the most salient features in the training data under some of the constraints described previously the model is encouraged to learn to precisely reproduce the most frequently observed characteristics when facing anomalies the model should worsen its reconstruction performance in most cases only data with normal instances are used to train the autoencoder in others the frequency of anomalies is small compared to the observation set so that its contribution to the learned representation could be ignored after training the autoencoder will accurately reconstruct normal data while failing to do so with unfamiliar anomalous data 44 reconstruction error the error between the original data and its low dimensional reconstruction is used as an anomaly score to detect anomalies 44 typically this means that on a validation set the empirical distribution of reconstruction errors is recorded and then e g the empirical 95 percentile x p displaystyle x_ p is taken as threshold t x p displaystyle t x_ p to flag anomalous data points loss x reconstruction x t anomaly displaystyle text loss x text reconstruction x t implies text anomaly since the threshold is an empirical quantile estimate there is an inherent difficulty with correctly setting this threshold in many cases the distribution of the empirical quantile is asymptotically a normal distribution empirical p quantile n μ p σ 2 p 1 p n f x p 2 displaystyle text empirical p quantile sim mathcal n left mu p sigma 2 frac p 1 p nf x_ p 2 right with f x p displaystyle f x_ p the probability density at the quantile this means that the variance grows if an extreme quantile is considered because f x p displaystyle f x_ p is small there this means that there is a potentially a big uncertainty in what is the right choice for the threshold since it is estimated from a validation set recent literature has however shown that certain autoencoding models can counterintuitively be very good at reconstructing anomalous examples and consequently not able to reliably perform anomaly detection 47 48 intuitively this can be understood by considering those one layer auto encoders which are related to pca also in this case there can be perfect rein reconstructions for points which are far away from the data region but which lie on a principal component axis it is best to analyze if the anomalies which are flagged by the auto encoder are true anomalies in this sense all the metrics in evaluation of binary classifiers can be considered the fundamental challenge which comes with the unsupervised self supervised learning setting is that labels for rare events do not exist in which case the labels first have to be gathered and the data set will be imbalanced or anomaly indicating labels are very rare introducing larger confidence intervals for these performance estimates image processing edit the characteristics of autoencoders are useful in image processing one example can be found in lossy image compression where autoencoders outperformed other approaches and proved competitive against jpeg 2000 49 50 another useful application of autoencoders in image preprocessing is image denoising 51 52 53 autoencoders found use in more demanding contexts such as medical imaging where they have been used for image denoising 54 as well as super resolution 55 56 in image assisted diagnosis experiments have applied autoencoders for breast cancer detection 57 and for modelling the relation between the cognitive decline of alzheimer s disease and the latent features of an autoencoder trained with mri 58 drug discovery edit in 2019 molecules generated with variational autoencoders were validated experimentally in mice 59 60 popularity prediction edit recently a stacked autoencoder framework produced promising results in predicting popularity of social media posts 61 which is helpful for online advertising strategies machine translation edit autoencoders have been applied to machine translation which is usually referred to as neural machine translation nmt 62 63 unlike traditional autoencoders the output does not match the input it is in another language in nmt texts are treated as sequences to be encoded into the learning procedure while on the decoder side sequences in the target language s are generated language specific autoencoders incorporate further linguistic features into the learning procedure such as chinese decomposition features 64 machine translation is rarely still done with autoencoders due to the availability of more effective transformer networks communication systems edit autoencoders in communication systems are important because they help in encoding data into a more resilient representation for channel impairments which is crucial for transmitting information while minimizing errors in addition ae based systems can optimize end to end communication performance this approach can solve the several limitations of designing communication systems such as the inherent difficulty in accurately modeling the complex behavior of real world channels 65 see also edit representation learning singular value decomposition sparse dictionary learning deep learning further reading edit bank dor koenigstein noam giryes raja 2023 autoencoders machine learning for data science handbook cham springer international publishing doi 10 1007 978 3 031 24628 9_16 isbn 978 3 031 24627 2 goodfellow ian bengio yoshua courville aaron 2016 14 autoencoders deep learning adaptive computation and machine learning cambridge mass the mit press isbn 978 0 262 03561 3 references edit bank dor koenigstein noam giryes raja 2023 autoencoders in rokach lior maimon oded shmueli erez eds machine learning for data science handbook pp 353 374 doi 10 1007 978 3 031 24628 9_16 isbn 978 3 031 24627 2 1 2 3 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