670 research outputs found
Unsupervised Domain Adaptation with Copula Models
We study the task of unsupervised domain adaptation, where no labeled data
from the target domain is provided during training time. To deal with the
potential discrepancy between the source and target distributions, both in
features and labels, we exploit a copula-based regression framework. The
benefits of this approach are two-fold: (a) it allows us to model a broader
range of conditional predictive densities beyond the common exponential family,
(b) we show how to leverage Sklar's theorem, the essence of the copula
formulation relating the joint density to the copula dependency functions, to
find effective feature mappings that mitigate the domain mismatch. By
transforming the data to a copula domain, we show on a number of benchmark
datasets (including human emotion estimation), and using different regression
models for prediction, that we can achieve a more robust and accurate
estimation of target labels, compared to recently proposed feature
transformation (adaptation) methods.Comment: IEEE International Workshop On Machine Learning for Signal Processing
201
Enhanced independent vector analysis for audio separation in a room environment
Independent vector analysis (IVA) is studied as a frequency domain blind source separation method, which can theoretically avoid the permutation problem by retaining the dependency between different frequency bins of the same source vector while removing the dependency between different source vectors. This thesis focuses upon improving the performance of independent vector analysis when it is used to solve the audio separation problem in a room environment.
A specific stability problem of IVA, i.e. the block permutation problem, is identified and analyzed. Then a robust IVA method is proposed to solve this problem by exploiting the phase continuity of the unmixing matrix. Moreover, an auxiliary function based IVA algorithm with an overlapped chain type source prior is proposed as well to mitigate this problem.
Then an informed IVA scheme is proposed which combines the geometric information of the sources from video to solve the problem by providing an intelligent initialization for optimal convergence. The proposed informed IVA algorithm can also achieve a faster convergence in terms of iteration numbers and better separation performance. A pitch based evaluation method is defined to judge the separation performance objectively when the information describing the mixing matrix and sources is missing.
In order to improve the separation performance of IVA, an appropriate multivariate source prior is needed to better preserve the dependency structure within the source vectors. A particular multivariate generalized Gaussian distribution is adopted as the source prior. The nonlinear score function derived from this proposed source prior contains the fourth order relationships between different frequency bins, which provides a more informative and stronger dependency structure compared with the original IVA algorithm and thereby improves the separation performance.
Copula theory is a central tool to model the nonlinear dependency structure. The t copula is proposed to describe the dependency structure within the frequency domain speech signals due to its tail dependency property, which means if one variable has an extreme value, other variables are expected to have extreme values. A multivariate student's t distribution constructed by using a t copula with the univariate student's t marginal distribution is proposed as the source prior. Then the IVA algorithm with the proposed source prior is derived.
The proposed algorithms are tested with real speech signals in different reverberant room environments both using modelled room impulse response and real room recordings. State-of-the-art criteria are used to evaluate the separation performance, and the experimental results confirm the advantage of the proposed algorithms
Two-step calibration method for multi-algorithm score-based face recognition systems by minimizing discrimination loss
We propose a new method for combining multi-algorithm score-based face recognition systems, which we call the two-step calibration method. Typically, algorithms for face recognition systems produce dependent scores. The two-step method is based on parametric copulas to handle this dependence. Its goal is to minimize discrimination loss. For synthetic and real databases (NIST-face and Face3D) we will show that our method is accurate and reliable using the cost of log likelihood ratio and the information-theoretical empirical cross-entropy (ECE)
CVAE: Gaussian Copula-based VAE Differing Disentangled from Coupled Representations with Contrastive Posterior
We present a self-supervised variational autoencoder (VAE) to jointly learn
disentangled and dependent hidden factors and then enhance disentangled
representation learning by a self-supervised classifier to eliminate coupled
representations in a contrastive manner. To this end, a Contrastive Copula VAE
(CVAE) is introduced without relying on prior knowledge about data in the
probabilistic principle and involving strong modeling assumptions on the
posterior in the neural architecture. CVAE simultaneously factorizes the
posterior (evidence lower bound, ELBO) with total correlation (TC)-driven
decomposition for learning factorized disentangled representations and extracts
the dependencies between hidden features by a neural Gaussian copula for copula
coupled representations. Then, a self-supervised contrastive classifier
differentiates the disentangled representations from the coupled
representations, where a contrastive loss regularizes this contrastive
classification together with the TC loss for eliminating entangled factors and
strengthening disentangled representations. CVAE demonstrates a strong
effect in enhancing disentangled representation learning. CVAE further
contributes to improved optimization addressing the TC-based VAE instability
and the trade-off between reconstruction and representation
A test for conditional correlation between random vectors based on weighted u-statistics
This article explores U-Statistics as a tool for testing conditional correlation between two multivariate sources with respect to a potential confounder. The proposed approach is effectively an instance of weighted U-Statistics and does not impose any statistical model on the processed data, in contrast to other well-known techniques that assume Gaussianity. By avoiding determinants and inverses, the method presented displays promising robustness in small-sample regimes. Its performance is evaluated numerically through its MSE and ROC curves.Peer ReviewedPostprint (author's final draft
Recommended from our members
Gaussian tree constraints applied to acoustic linguistic functional data
Evolutionary models of languages are usually considered to take the form of
trees. With the development of so-called tree constraints the plausibility of
the tree model assumptions can be addressed by checking whether the moments of
observed variables lie within regions consistent with trees. In our linguistic
application, the data set comprises acoustic samples (audio recordings) from
speakers of five Romance languages or dialects. We wish to assess these
functional data for compatibility with a hereditary tree model at the language
level. A novel combination of canonical function analysis (CFA) with a
separable covariance structure provides a method for generating a
representative basis for the data. This resulting basis is formed of components
which emphasize language differences whilst maintaining the integrity of the
observational language-groupings. A previously unexploited Gaussian tree
constraint is then applied to component-by-component projections of the data to
investigate adherence to an evolutionary tree. The results indicate that while
a tree model is unlikely to be suitable for modeling all aspects of the
acoustic linguistic data, certain features of the spoken Romance languages
highlighted by the separable-CFA basis may indeed be suitably modeled as a
tree.NS acknowledges the support of Economics and Social Science Research Council grant ES/I90427/1. JADA acknowledges the support of UK Engineering and Physical Sciences Research Council grant EP/K021672/2. JSC acknowledges the support of UK Arts and Humanities Research Council grant AH/M002993/1.This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.jmva.2016.09.01
- …