6,418 research outputs found
Mixtures of Shifted Asymmetric Laplace Distributions
A mixture of shifted asymmetric Laplace distributions is introduced and used
for clustering and classification. A variant of the EM algorithm is developed
for parameter estimation by exploiting the relationship with the general
inverse Gaussian distribution. This approach is mathematically elegant and
relatively computationally straightforward. Our novel mixture modelling
approach is demonstrated on both simulated and real data to illustrate
clustering and classification applications. In these analyses, our mixture of
shifted asymmetric Laplace distributions performs favourably when compared to
the popular Gaussian approach. This work, which marks an important step in the
non-Gaussian model-based clustering and classification direction, concludes
with discussion as well as suggestions for future work
Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition
This paper presents a novel quadratic projection based feature extraction
framework, where a set of quadratic matrices is learned to distinguish each
class from all other classes. We formulate quadratic matrix learning (QML) as a
standard semidefinite programming (SDP) problem. However, the con- ventional
interior-point SDP solvers do not scale well to the problem of QML for
high-dimensional data. To solve the scalability of QML, we develop an efficient
algorithm, termed DualQML, based on the Lagrange duality theory, to extract
nonlinear features. To evaluate the feasibility and effectiveness of the
proposed framework, we conduct extensive experiments on biometric recognition.
Experimental results on three representative biometric recogni- tion tasks,
including face, palmprint, and ear recognition, demonstrate the superiority of
the DualQML-based feature extraction algorithm compared to the current
state-of-the-art algorithm
The detection of patients at risk of gastrointestinal toxicity during pelvic radiotherapy by electronic nose and FAIMS : a pilot study
It is well known that the electronic nose can be used to identify differences between human health and disease for a range of disorders. We present a pilot study to investigate if the electronic nose and a newer technology, FAIMS (Field Asymmetric Ion Mobility Spectrometry), can be used to identify and help inform the treatment pathway for patients receiving pelvic radiotherapy, which frequently causes gastrointestinal side-effects, severe in some. From a larger group, 23 radiotherapy patients were selected where half had the highest levels of toxicity and the others the lowest. Stool samples were obtained before and four weeks after radiotherapy and the volatiles and gases emitted analysed by both methods; these chemicals are products of fermentation caused by gut microflora. Principal component analysis of the electronic nose data and wavelet transform followed by Fisher discriminant analysis of FAIMS data indicated that it was possible to separate patients after treatment by their toxicity levels. More interestingly, differences were also identified in their pre-treatment samples. We believe these patterns arise from differences in gut microflora where some combinations of bacteria result to give this olfactory signature. In the future our approach may result in a technique that will help identify patients at āhigh riskā even before radiation treatment is started
Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction
It is difficult to find the optimal sparse solution of a manifold learning
based dimensionality reduction algorithm. The lasso or the elastic net
penalized manifold learning based dimensionality reduction is not directly a
lasso penalized least square problem and thus the least angle regression (LARS)
(Efron et al. \cite{LARS}), one of the most popular algorithms in sparse
learning, cannot be applied. Therefore, most current approaches take indirect
ways or have strict settings, which can be inconvenient for applications. In
this paper, we proposed the manifold elastic net or MEN for short. MEN
incorporates the merits of both the manifold learning based dimensionality
reduction and the sparse learning based dimensionality reduction. By using a
series of equivalent transformations, we show MEN is equivalent to the lasso
penalized least square problem and thus LARS is adopted to obtain the optimal
sparse solution of MEN. In particular, MEN has the following advantages for
subsequent classification: 1) the local geometry of samples is well preserved
for low dimensional data representation, 2) both the margin maximization and
the classification error minimization are considered for sparse projection
calculation, 3) the projection matrix of MEN improves the parsimony in
computation, 4) the elastic net penalty reduces the over-fitting problem, and
5) the projection matrix of MEN can be interpreted psychologically and
physiologically. Experimental evidence on face recognition over various popular
datasets suggests that MEN is superior to top level dimensionality reduction
algorithms.Comment: 33 pages, 12 figure
Mixtures of Skew-t Factor Analyzers
In this paper, we introduce a mixture of skew-t factor analyzers as well as a
family of mixture models based thereon. The mixture of skew-t distributions
model that we use arises as a limiting case of the mixture of generalized
hyperbolic distributions. Like their Gaussian and t-distribution analogues, our
mixture of skew-t factor analyzers are very well-suited to the model-based
clustering of high-dimensional data. Imposing constraints on components of the
decomposed covariance parameter results in the development of eight flexible
models. The alternating expectation-conditional maximization algorithm is used
for model parameter estimation and the Bayesian information criterion is used
for model selection. The models are applied to both real and simulated data,
giving superior clustering results compared to a well-established family of
Gaussian mixture models
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