A New Information-Theoretic Based Ica Algorithm For Blind Signal Separation
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Bibliographic record
Abstract
AbstractThe typical information-theoretic approaches such as INFO MAX and MMI perform independent component analysis (ICA) by using a fixed nonlinearity function. Consequently, they can only separate either sub-Gaussian or super-Gaussian source signals, but not both. This article considers a flexible nonlinearity function that is a single polynomial term with the exponent learnable. The separation ability of this function is analysed, and a new ICA algorithm is proposed. The experiments have shown that this algorithm can successfully separate the mixture of sub-Gaussian and super-Gaussian sources.Key Words: Independent component analysisblind signal separationflexible nonlinearity functionsub-Gaussiansuper-Gaussian sources Additional informationNotes on contributorsY.-M. CheungYiu-ming Cheung received Ph.D. degree in computer science and engineering from the Chinese University of Hong Kong in 2000. Currently, he is Assistant Professor in the Department of Computer Science, Hong Kong Baptist University His research interests include independent component iz analysis, multivariate data clustering analysis, radial basis function networks, time series analysis, portfolio management, and automated trading system.L. XuLei Xu (IEEE Fellow) is a Professor of Department of Computer Science and Engineering at Chinese University of Hong Kong (CUHK). He is also a full Professor at Peking University, and an adjunct Professor at three other universities in China and UK. After receiving his Ph.D. from Tsinghua University in early 1987, he joined Peking University, where he became one of ten university-level exceptionally promoted young associate professors in 1988 and further been exceptionally promoted to a full Professor in 1992. During 1989-1993, he worked at several universities in Finland, Canada and USA, including Harvard and MIT. He joined CUHK in 1993 as a Senior Lecturer and then took the current professor position since 1996.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it