1,052 research outputs found
Structure-Aware Classification using Supervised Dictionary Learning
In this paper, we propose a supervised dictionary learning algorithm that
aims to preserve the local geometry in both dimensions of the data. A
graph-based regularization explicitly takes into account the local manifold
structure of the data points. A second graph regularization gives similar
treatment to the feature domain and helps in learning a more robust dictionary.
Both graphs can be constructed from the training data or learned and adapted
along the dictionary learning process. The combination of these two terms
promotes the discriminative power of the learned sparse representations and
leads to improved classification accuracy. The proposed method was evaluated on
several different datasets, representing both single-label and multi-label
classification problems, and demonstrated better performance compared with
other dictionary based approaches
Joint Bayesian Gaussian discriminant analysis for speaker verification
State-of-the-art i-vector based speaker verification relies on variants of
Probabilistic Linear Discriminant Analysis (PLDA) for discriminant analysis. We
are mainly motivated by the recent work of the joint Bayesian (JB) method,
which is originally proposed for discriminant analysis in face verification. We
apply JB to speaker verification and make three contributions beyond the
original JB. 1) In contrast to the EM iterations with approximated statistics
in the original JB, the EM iterations with exact statistics are employed and
give better performance. 2) We propose to do simultaneous diagonalization (SD)
of the within-class and between-class covariance matrices to achieve efficient
testing, which has broader application scope than the SVD-based efficient
testing method in the original JB. 3) We scrutinize similarities and
differences between various Gaussian PLDAs and JB, complementing the previous
analysis of comparing JB only with Prince-Elder PLDA. Extensive experiments are
conducted on NIST SRE10 core condition 5, empirically validating the
superiority of JB with faster convergence rate and 9-13% EER reduction compared
with state-of-the-art PLDA.Comment: accepted by ICASSP201
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