Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Deterministic Independent Component Analysis
Authors: Ruitong Huang, Andras Gyorgy, Csaba Szepesvári
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results are reported in Section 6. |
| Researcher Affiliation | Academia | Department of Computing Science, University of Alberta, Edmonton, AB T6G2E8 Canada |
| Pseudocode | Yes | Algorithm 1 The HKICA algorithm. input x(t) for 1 t T. output An estimation of the mixing matrix A.; Algorithm 2 Deterministic ICA (DICA) input x(t) for 1 t T. output An estimation of the mixing matrix A.; Algorithm 3 Recursive version of HKICA (HKICA.R) input x(t) for 1 t T. output An estimation of the mixing matrix A.; Algorithm 4 The Decompose helper function |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper uses synthetically generated data rather than a publicly available dataset: 'We generate a 6-dimensional BPSK signal s as follows. Let p = ( 19). We generate a {+1, 1} valued sequence q(t) uniformly at random for 1 t T, and set si(t) = q(t)i sin(pit).' |
| Dataset Splits | No | The paper generates data ('We take T = 20000 instances of the observed signal') but does not specify how this data is split into training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'ITE toolbox (Szab o et al., 2012)' and 'recursive Fourier PCA algorithm of Xiao (2014)' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We take T = 20000 instances of the observed signal on time steps t = 1, . . . , 20000. We test the noise ratio c from 0 (noise-free) to 1 (very noisy). All the algorithms are evaluated on a 150 repetitions. For each repetition, we try 3 times and report the best. |