Principal Orthogonal Latent Components Analysis (POLCA Net)
<|reference_start|>Principal Orthogonal Latent Components Analysis (POLCA Net): Representation learning is a pivotal area in the field of machine learning, focusing on the development of methods to automatically discover the representations or features needed for a given task from raw data. Unlike traditional feature engineering, which requires manual crafting of features, representation learning aims to learn features that are more useful and relevant for tasks such as classification, prediction, and clustering. We introduce Principal Orthogonal Latent Components Analysis Network (POLCA Net), an approach to mimic and extend PCA and LDA capabilities to non-linear domains. POLCA Net combines an autoencoder framework with a set of specialized loss functions to achieve effective dimensionality reduction, orthogonality, variance-based feature sorting, high-fidelity reconstructions, and additionally, when used with classification labels, a latent representation well suited for linear classifiers and low dimensional visualization of class distribution as well.<|reference_end|>
@article{h.2024principal,
    title={Principal Orthogonal Latent Components Analysis (POLCA Net)},
    author={Jose Antonio Martin H. and Freddy Perozo and Manuel Lopez},
    journal={arXiv preprint arXiv:2410.07289},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07289},
    primaryClass={cs.LG cs.AI}
}