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Data mining by nonnegative tensor approximation
- Source :
- MLSP, MLSP 2014-IEEE 24th International Workshop on Machine Learning for Signal Processing, MLSP 2014-IEEE 24th International Workshop on Machine Learning for Signal Processing, Sep 2014, Reims, France, HAL
- Publication Year :
- 2014
- Publisher :
- IEEE, 2014.
-
Abstract
- International audience; Inferring multilinear dependences within multi-way data can be performed by tensor decompositions. Because of the presence of noise or modeling errors, the problem actually requires an approximation of lower rank. We concentrate on the case of real 3-way data arrays with nonnegative values, and propose an unconstrained algorithm resorting to an hyperspherical parameterization implemented in a novel way, and to a global line search. To illustrate the contribution, we report computer experiments allowing to detect and identify toxic molecules in a solvent with the help of fluorescent spectroscopy measurements.
- Subjects :
- Multilinear map
Mathematical optimization
Rank (linear algebra)
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing
02 engineering and technology
low-rank
01 natural sciences
muti-way
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
Tensor (intrinsic definition)
0202 electrical engineering, electronic engineering, information engineering
Nonnegative tensor
Polycyclic Aromatic Hydrocarbons
approximation
Mathematics
Line search
010401 analytical chemistry
020206 networking & telecommunications
HAP
Computer experiment
tensor
0104 chemical sciences
CP
nonnegative
fluorescence
line search
Noise (video)
Algorithm
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
- Accession number :
- edsair.doi.dedup.....b57554b4a82e64a17d58bcd76b96a488