Abstract

Kernelized canonical correlations analysis has been utilized as a means to carry out unsupervised data clustering. This work puts forth a method to obtain a non-linear mapping that transforms data to a feature space where the data has the potential to be more ‘linearly’ correlated. An auto encoder network is employed to minimize a reconstruction error and learn a proper non-linear mapping to transform the input data. The non-linear mapping found via the novel scheme goes beyond kernel functions and results in better clustering performance than linear and kernel-based canonical correlations as demonstrated by numerical tests conducted across two different data sets.

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Bibtex

@inproceedings{malhotra2024deeplinearization,
  title     = {Deep Linearization Mappings for Canonical Correlation Clustering},
  author    = {Malhotra, Akshay and Schizas, Ioannis D. and Morais, Jo{\~a}o},
  booktitle = {2024 58th Asilomar Conference on Signals, Systems, and Computers},
  pages     = {1095--1099},
  year      = {2024},
  publisher = {IEEE},
  doi       = {10.1109/IEEECONF60004.2024.10942879},
  url       = {https://ieeexplore.ieee.org/document/10942879}
}