15,548 research outputs found
Detection of fast radio transients with multiple stations: a case study using the Very Long Baseline Array
Recent investigations reveal an important new class of transient radio
phenomena that occur on sub-millisecond timescales. Often transient surveys'
data volumes are too large to archive exhaustively. Instead, an on-line
automatic system must excise impulsive interference and detect candidate events
in real-time. This work presents a case study using data from multiple
geographically distributed stations to perform simultaneous interference
excision and transient detection. We present several algorithms that
incorporate dedispersed data from multiple sites, and report experiments with a
commensal real-time transient detection system on the Very Long Baseline Array
(VLBA). We test the system using observations of pulsar B0329+54. The
multiple-station algorithms enhanced sensitivity for detection of individual
pulses. These strategies could improve detection performance for a future
generation of geographically distributed arrays such as the Australian Square
Kilometre Array Pathfinder and the Square Kilometre Array.Comment: 12 pages, 14 figures. Accepted for Ap
Influence of observations on the misclassification probability in quadratic discriminant analysis.
In this paper it is analyzed how observations in the training sample affect the misclassification probability of a quadratic discriminant rule. An approach based on partial influence functions is followed. It allows to quantify the effect of observations in the training sample on the quality of the associated classification rule. Focus is more on the effect on the future misclassification rate, than on the influence on the parameters of the quadratic discriminant rule. The expression for the influence function is then used to construct a diagnostic tool for detecting influential observations. Applications on real data sets are provided.Applications; Classification; Data; Diagnostics; Discriminant analysis; Functions; Influence function; Misclassification probability; Outliers; Partial influence functions; Probability; Quadratic discriminant analysis; Quality; Robust covariance estimation; Robust regression; Training;
Influence of observations on the misclassification probability in quadratic discriminant analysis.
In this paper it is studied how observations in the training sample affect the misclassification probability of a quadratic discriminant rule. An approach based on partial influence functions is followed. It allows to quantify the effect of observations in the training sample on the performance of the associated classification rule. Focus is on the effect of outliers on the misclassification rate, merely than on the estimates of the parameters of the quadratic discriminant rule. The expression for the partial influence function is then used to construct a diagnostic tool for detecting influential observations. Applications on real data sets are provided.Applications; Classification; Data; Diagnostics; Discriminant analysis; Estimator; Functions; Influence function; Misclassification probability; Outliers; Parameters; Partial influence functions; Performance; Principal components; Probability; Quadratic discriminant analysis; Tool; Training;
Discriminant analysis under the common principal components model
For two or more populations of which the covariance matrices have a common set of eigenvectors, but different sets of eigenvalues, the common principal components (CPC) model is appropriate. Pepler et al. (2015) proposed a regularised CPC covariance matrix estimator and showed that this estimator outperforms the unbiased and pooled estimators in situations where the CPC model is applicable. This paper extends their work to the context of discriminant analysis for two groups, by plugging the regularised CPC estimator into the ordinary quadratic discriminant function. Monte Carlo simulation results show that CPC discriminant analysis offers significant improvements in misclassification error rates in certain situations, and at worst performs similar to ordinary quadratic and linear discriminant analysis. Based on these results, CPC discriminant analysis is recommended for situations where the sample size is small compared to the number of variables, in particular for cases where there is uncertainty about the population covariance matrix structures
Person Re-identification by Local Maximal Occurrence Representation and Metric Learning
Person re-identification is an important technique towards automatic search
of a person's presence in a surveillance video. Two fundamental problems are
critical for person re-identification, feature representation and metric
learning. An effective feature representation should be robust to illumination
and viewpoint changes, and a discriminant metric should be learned to match
various person images. In this paper, we propose an effective feature
representation called Local Maximal Occurrence (LOMO), and a subspace and
metric learning method called Cross-view Quadratic Discriminant Analysis
(XQDA). The LOMO feature analyzes the horizontal occurrence of local features,
and maximizes the occurrence to make a stable representation against viewpoint
changes. Besides, to handle illumination variations, we apply the Retinex
transform and a scale invariant texture operator. To learn a discriminant
metric, we propose to learn a discriminant low dimensional subspace by
cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is
learned on the derived subspace. We also present a practical computation method
for XQDA, as well as its regularization. Experiments on four challenging person
re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show
that the proposed method improves the state-of-the-art rank-1 identification
rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.Comment: This paper has been accepted by CVPR 2015. For source codes and
extracted features please visit
http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda
Sufficient Covariate, Propensity Variable and Doubly Robust Estimation
Statistical causal inference from observational studies often requires
adjustment for a possibly multi-dimensional variable, where dimension reduction
is crucial. The propensity score, first introduced by Rosenbaum and Rubin, is a
popular approach to such reduction. We address causal inference within Dawid's
decision-theoretic framework, where it is essential to pay attention to
sufficient covariates and their properties. We examine the role of a propensity
variable in a normal linear model. We investigate both population-based and
sample-based linear regressions, with adjustments for a multivariate covariate
and for a propensity variable. In addition, we study the augmented inverse
probability weighted estimator, involving a combination of a response model and
a propensity model. In a linear regression with homoscedasticity, a propensity
variable is proved to provide the same estimated causal effect as multivariate
adjustment. An estimated propensity variable may, but need not, yield better
precision than the true propensity variable. The augmented inverse probability
weighted estimator is doubly robust and can improve precision if the propensity
model is correctly specified
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
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