The 2018 International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA, will be the 14th in a series of interdisciplinary conferences which have attracted hundreds of researchers and practitioners over the years.
The proceedings will be published in Springer-Verlag’s Lecture Notes in Computer Science (LNCS). Prospective authors are invited to submit original papers related to the topics outlined below and should be 8-10 pages in LNCS format. Papers must be original and must not be already published nor under review elsewhere.
Topics
Prospective authors are invited to submit original papers in all areas related to latent variable analysis, independent component analysis and signal separation, including but not limited to:
Theory:
- Sparse coding, dictionary learning
- Statistical and probabilistic modeling
- Detection, estimation and performance criteria and bounds
- Causality measures
- Learning theory
- Convex/nonconvex optimization tools
- Sketching and censoring for large scale data
Models:
- General linear or nonlinear models of signals and data
- Discrete, continuous, flat, or hierarchical models
- Multilinear models
- Time-varying, instantaneous, convolutive, noiseless, noisy, over-complete, or under-complete mixtures
- Low-rank models, graph models, online models
Algorithms:
- Estimation, separation, identification, detection, blind and semi-blind methods, non-negative matrix factorization, tensor decomposition, adaptive and recursive estimation
- Feature selection
- Time-frequency and wavelet based analysis
- Complexity analysis
Applications:
- Speech and audio separation, recognition, dereverberation and denoising
- Auditory scene analysis
- Image segmentation, separation, fusion, classification, texture analysis
- Biomedical signal analysis, imaging, genomic data analysis, brain-computer interface
- Non-conventional signals (e.g. graph signals, quantum sources)
Emerging related topics:
- Sparse learning
- Deep learning
- Social networks
- Data mining
- Artificial intelligence
- Objective and subjective performance evaluation
