Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing
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Abstract (arXiv)
We develop a new neural network based independent component analysis (ICA) method by directly minimizing the dependence amongst all extracted components. Using the matrix-based R'enyi's α-order entropy functional, our network can be directly optimized by stochastic gradient descent (SGD), without any variational approximation or adversarial training. As a solid application, we evaluate our ICA in the problem of hyperspectral unmixing (HU) and refute a statement that "ICA does not play a role in unmixing hyperspectral data", which was initially suggested by citenascimento2005does. Code and additional remarks of our DDICA is available at https://github.com/hongmingli1995/DDICA.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 10 of its 32 references that are in the map. First posted on arXiv 7 Feb 2022.
Builds on
10 of 32 references.
References · 10
- 2018MINE: Mutual Information Neural Estimationcited by 683
- 2018Invariant Information Clustering for Unsupervised Image Classification and Segmentationcited by 455
- 2018Isolating Sources of Disentanglement in Variational Autoencoderscited by 167
- 2017Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Surveycited by 33
- 2017Learning Independent Features with Adversarial Nets for Non-linear ICAcited by 33
- 2021Measuring Dependence with Matrix-based Entropy Functionalcited by 19
- 2018Multivariate Extension of Matrix-based Renyi's α-order Entropy Functionalcited by 6
- 2018Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whiteningcited by 6
- 2020Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing incorporating Endmember Independencecited by 3
- 2019Learning gradient-based ICA by neurally estimating mutual informationcited by 2
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.