JADE for Tensor-Valued Observations
| Autor: | Joni Virta, Bing Li, Klaus Nordhausen, Hannu Oja |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Multilinear algebra ta112 Computer science 62H12 (Primary) 62G20 62H10 (Secondary) Estimator Mathematics - Statistics Theory 020206 networking & telecommunications Statistics Theory (math.ST) 02 engineering and technology Version vector 01 natural sciences Independent component analysis 010104 statistics & probability Component analysis FOS: Mathematics 0202 electrical engineering electronic engineering information engineering Kurtosis Discrete Mathematics and Combinatorics Noise (video) Tensor 0101 mathematics Statistics Probability and Uncertainty Algorithm |
| Zdroj: | Journal of Computational and Graphical Statistics. 27:628-637 |
| ISSN: | 1061-8600 |
| DOI: | 10.1080/10618600.2017.1407324 |
| Popis: | Independent component analysis is a standard tool in modern data analysis and numerous different techniques for applying it exist. The standard methods however quickly lose their effectiveness when the data are made up of structures of higher order than vectors, namely matrices or tensors (for example, images or videos), being unable to handle the high amounts of noise. Recently, an extension of the classic fourth order blind identification (FOBI) specifically suited for tensor-valued observations was proposed and showed to outperform its vector version for tensor data. In this paper we extend another popular independent component analysis method, the joint approximate diagonalization of eigen-matrices (JADE), for tensor observations. In addition to the theoretical background we also provide the asymptotic properties of the proposed estimator and use both simulations and real data to show its usefulness and superiority over its competitors. 10 pages, 3 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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