A fast fixed-point algorithm for independent component analysis
Neural Computation
Morphological bidirectional associative memories
Neural Networks
Mean-field approaches to independent component analysis
Neural Computation
Reconstruction of Patterns from Noisy Inputs Using Morphological Associative Memories
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision
Independent Component Analysis Applied to fMRI Data: A Generative Model for Validating Results
Journal of VLSI Signal Processing Systems
A single individual evolutionary strategy for endmember search in hyperspectral images
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: FEA 2002
Blind detection of independent dynamic components
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery
IEEE Transactions on Signal Processing
Information Sciences: an International Journal
A lattice matrix method for hyperspectral image unmixing
Information Sciences: an International Journal
IEEE Transactions on Signal Processing - Part I
Morphological associative memories
IEEE Transactions on Neural Networks
Gray-scale morphological associative memories
IEEE Transactions on Neural Networks
High resolution segmentation of csf on phase contrast MRI
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Exploration of LICA detections in resting state fMRI
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Experiments on lattice independent component analysis for face recognition
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Computers in Biology and Medicine
A comparison of VBM results by SPM, ICA and LICA
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
An endmember-based distance for content based hyperspectral image retrieval
Pattern Recognition
Hybrid computational methods for hyperspectral image analysis
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
A novel lattice associative memory based on dendritic computing
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer's disease
Pattern Recognition Letters
Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering
Information Sciences: an International Journal
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We introduce a lattice independent component analysis (LICA) unsupervised scheme to functional magnetic resonance imaging (fMRI) data analysis. LICA is a non-linear alternative to independent component analysis (ICA), such that ICA's statistical independent sources correspond to LICA's lattice independent sources. In this paper, LICA uses an incremental lattice source induction algorithm (ILSIA) to induce the lattice independent sources from the input dataset. The ILSIA computes a set of Strongly Lattice Independent vectors using properties of lattice associative memories regarding Lattice Independence and Chebyshev best approximation. The lattice independent sources constitute a set of Affine Independent vectors that define a simplex covering the input data. LICA carries out data linear unmixing based on the lattice independent sources basis. Therefore, LICA is a hybrid combination of a non-linear lattice based component and a linear unmixing component. The principal advantage over ICA is that LICA does not impose any probabilistic model assumptions on the data sources. We compare LICA with ICA in two case studies. Firstly, including simulated fMRI data, LICA discovers the spatial location of meaningful sources with less ambiguity than ICA. Secondly, including real data from an auditory stimulation experiment, LICA improves over some state of the art ICA variants discovering the activation patterns detected by Statistical Parametric Mapping (SPM) on the same data.