Covariance shrinkage for autocorrelated data
Authors: Daniel Bartz, Klaus-Robert Müller
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose an alternative estimator, which is (1) unbiased, (2) less sensitive to hyperparameter choice and (3) yields superior performance in simulations on toy data and on a real world data set from an EEG-based Brain-Computer-Interfacing experiment. |
| Researcher Affiliation | Academia | Daniel Bartz Department of Computer Science TU Berlin, Berlin, Germany daniel.bartz@tu-berlin.de Klaus-Robert M uller TU Berlin, Berlin, Germany Korea University, Korea, Seoul klaus-robert.mueller@tu-berlin.de |
| Pseudocode | No | The paper presents mathematical formulas and descriptions of estimators but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific repository links or explicit statements about the public release of the source code for the methodology described. |
| Open Datasets | Yes | As an example of autocorrelated data we reanalyzed a data set from a motor imagery experiment. In the experiment, brain activity for two different imagined movements was measured via EEG (p = 55 channels, 80 subjects, 150 trials per subject, each trial with ntrial = 390 measurements [BSH+10]). |
| Dataset Splits | No | The paper analyzes performance based on the 'number of trials per class' (training data size) and mentions cross-validation as a baseline method. However, it does not provide specific dataset split information (exact percentages, sample counts, or predefined split citations) needed to reproduce the data partitioning for their own experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments or simulations. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Our simulations are based on those in [San08]: We average over R = 50 multivariate Gaussian AR(1) models xt = A xt 1 + ϵt, with parameter matrix4 A = ψAC I , with ψno AC = 0, ψlow AC = 0.7, and ψhigh AC = 0.95 (see Figure 1). The innovations ϵit are Gaussian with variances σ2 i drawn from a log-normal distribution with mean µ = 1 and scale parameter σ = 0.5. For each model, we generate K = 50 data sets to calculate the std. deviations of the estimators and to obtain an approximation of p 2 P ij Var (Sij). ... The number of observations is fixed at n = 250 and the lag parameter b chosen by hand such that the whole autocorrelation is covered5: bno AC = 10, blow AC = 20 and bhigh AC = 90. ... for a motor imagery experiment. In the experiment, brain activity for two different imagined movements was measured via EEG (p = 55 channels, 80 subjects, 150 trials per subject, each trial with ntrial = 390 measurements [BSH+10]). ... The frequency band was optimized for each subject and from the class-wise covariance matrices, 1-3 filters per class were extracted by Common Spatial Patterns (CSP), adaptively chosen by a heuristic... We trained Linear Discriminant Analysis on log-variance features. ... an optimized time lag (b = 75) ... a too large time lag (b = 300). |