section about preprocessing, TODO plot projection

thesis/bib/bibliography.bib

@@ -167,4 +167,17 @@
   volume = {61},
   pages = { 85-117 },
   url = {https://api.semanticscholar.org/CorpusID:11715509},
+},
+@article{autoencoder_survey,
+title = {A comprehensive survey on design and application of autoencoder in deep learning},
+journal = {Applied Soft Computing},
+volume = {138},
+pages = {110176},
+year = {2023},
+issn = {1568-4946},
+doi = {https://doi.org/10.1016/j.asoc.2023.110176},
+url = {https://www.sciencedirect.com/science/article/pii/S1568494623001941},
+author = {Pengzhi Li and Yan Pei and Jianqiang Li},
+keywords = {Deep learning, Autoencoder, Unsupervised learning, Feature extraction, Autoencoder application},
+abstract = {Autoencoder is an unsupervised learning model, which can automatically learn data features from a large number of samples and can act as a dimensionality reduction method. With the development of deep learning technology, autoencoder has attracted the attention of many scholars. Researchers have proposed several improved versions of autoencoder based on different application fields. First, this paper explains the principle of a conventional autoencoder and investigates the primary development process of an autoencoder. Second, We proposed a taxonomy of autoencoders according to their structures and principles. The related autoencoder models are comprehensively analyzed and discussed. This paper introduces the application progress of autoencoders in different fields, such as image classification and natural language processing, etc. Finally, the shortcomings of the current autoencoder algorithm are summarized, and prospected for its future development directions are addressed.}
 }