Parsimonious Shifted Asymmetric Laplace Mixtures

A family of parsimonious shifted asymmetric Laplace mixture models is introduced. We extend the mixture of factor analyzers model to the shifted asymmetric Laplace distribution. Imposing constraints on the constitute parts of the resulting decomposed component scale matrices leads to a family of parsimonious models. An explicit two-stage parameter estimation procedure is described, and the Bayesian information criterion and the integrated completed likelihood are compared for model selection. This novel family of models is applied to real data, where it is compared to its Gaussian analogue within clustering and classification paradigms

Unsupervised Learning via Mixtures of Skewed Distributions with Hypercube Contours

Mixture models whose components have skewed hypercube contours are developed via a generalization of the multivariate shifted asymmetric Laplace density. Specifically, we develop mixtures of multiple scaled shifted asymmetric Laplace distributions. The component densities have two unique features: they include a multivariate weight function, and the marginal distributions are also asymmetric Laplace. We use these mixtures of multiple scaled shifted asymmetric Laplace distributions for clustering applications, but they could equally well be used in the supervised or semi-supervised paradigms. The expectation-maximization algorithm is used for parameter estimation and the Bayesian information criterion is used for model selection. Simulated and real data sets are used to illustrate the approach and, in some cases, to visualize the skewed hypercube structure of the components

LoCuSS: Calibrating Mass-Observable Scaling Relations for Cluster Cosmology with Subaru Weak Lensing Observations

We present a joint weak-lensing/X-ray study of galaxy cluster mass-observable scaling relations, motivated by the critical importance of accurate calibration of mass proxies for future X-ray missions, including eROSITA. We use a sample of 12 clusters at z\simeq0.2 that we have observed with Subaru and XMM-Newton to construct relationships between the weak-lensing mass (M), and three X-ray observables: gas temperature (T), gas mass (Mgas), and quasi-integrated gas pressure (Yx) at overdensities of \Delta=2500, 1000, and 500 with respect to the critical density. We find that Mgas at \Delta\le1000 appears to be the most promising mass proxy of the three, because it has the lowest intrinsic scatter in mass at fixed observable: \sigma_lnM\simeq0.1, independent of cluster dynamical state. The scatter in mass at fixed T and Yx is a factor of \sim2-3 larger than at fixed Mgas, which are indicative of the structural segregation that we find in the M-T and M-Yx relationships. Undisturbed clusters are found to be \sim40% and \sim20% more massive than disturbed clusters at fixed T and Yx respectively at \sim2\sigma significance. In particular, A1914 - a well-known merging cluster - significantly increases the scatter and lowers the the normalization of the relation for disturbed clusters. We also investigated the covariance between intrinsic scatter in M-Mgas and M-T relations, finding that they are positively correlated. This contradicts the adaptive mesh refinement simulations that motivated the idea that Yx may be a low scatter mass proxy, and agrees with more recent smoothed particle hydrodynamic simulations based on the Millennium Simulation. We also propose a method to identify a robust mass proxy based on principal component analysis. The statistical precision of our results are limited by the small sample size and the presence of the extreme merging cluster in our sample.Comment: 13 pages, 6 figures : ApJ in press : proof ve

Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data

Hyperspectral data allows the construction of more elaborate models to sample the properties of the nonferrous materials than the standard RGB color representation. In this paper, the nonferrous waste materials are studied as they cannot be sorted by classical procedures due to their color, weight and shape similarities. The experimental results presented in this paper reveal that factors such as the various levels of oxidization of the waste materials and the slight differences in their chemical composition preclude the use of the spectral features in a simplistic manner for robust material classification. To address these problems, the proposed FUSSER (fuzzy spectral and spatial classifier) algorithm detailed in this paper merges the spectral and spatial features to obtain a combined feature vector that is able to better sample the properties of the nonferrous materials than the single pixel spectral features when applied to the construction of multivariate Gaussian distributions. This approach allows the implementation of statistical region merging techniques in order to increase the performance of the classification process. To achieve an efficient implementation, the dimensionality of the hyperspectral data is reduced by constructing bio-inspired spectral fuzzy sets that minimize the amount of redundant information contained in adjacent hyperspectral bands. The experimental results indicate that the proposed algorithm increased the overall classification rate from 44% using RGB data up to 98% when the spectral-spatial features are used for nonferrous material classification

A morphological approach for segmentation and tracking of human faces

A new technique for segmenting and tracking human faces in video sequences is presented. The technique relies on morphological tools such as using connected operators to extract the connected component that more likely belongs to a face, and partition projection to track this component through the sequence. A binary partition tree (BPT) is used to implement the connected operator. The BPT is constructed based on the chrominance criteria and its nodes are analyzed so that the selected node maximizes an estimation of the likelihood of being part of a face. The tracking is performed using a partition projection approach. Images are divided into face and non-face parts, which are tracked through the sequence. The technique has been successfully assessed using several test sequences from the MPEG-4 (raw format) and the MPEG-7 databases (MPEG-1 format).Peer ReviewedPostprint (published version

Subaru weak-lensing study of A2163: bimodal mass structure

We present a weak-lensing analysis of the merging cluster A2163 using Subaru/Suprime-Cam and CFHT/Mega-Cam data and discuss the dynamics of this cluster merger, based on complementary weak-lensing, X-ray, and optical spectroscopic datasets. From two dimensional multi-component weak-lensing analysis, we reveal that the cluster mass distribution is well described by three main components, including a two component main cluster A2163-A with mass ratio 1:8, and its cluster satellite A2163-B. The bimodal mass distribution in A2163-A is similar to the galaxy density distribution, but appears as spatially segregated from the brightest X-ray emitting gas region. We discuss the possible origins of this gas-dark matter offset and suggest the gas core of the A2163-A subcluster has been stripped away by ram pressure from its dark matter component. The survival of this gas core to the tidal forces exerted by the main cluster let us infer a subcluster accretion with a non-zero impact parameter. Dominated by the most massive component of A2163-A, the mass distribution of A2163 is well described by a universal Navarro-Frenk-White profile as shown by a one-dimensional tangential shear analysis, while the singular-isothermal sphere profile is strongly ruled out. Comparing this cluster mass profile with profiles derived assuming intracluster medium hydrostatic equilibrium (H.E.) in two opposite regions of the cluster atmosphere has allowed us to confirm the prediction of a departure from H.E. in the eastern cluster side, presumably due to shock heating. Yielding a cluster mass estimate of M_{500}=11.18_{-1.46}^{+1.64}\times10^{14}h^{-1}Msun, our mass profile confirm the exceptionally high mass of A2163, consistent with previous analyses relying on the cluster dynamical analysis and Yx mass proxy.Comment: 17 pages, 11 figures, ApJ, in press. Full resolution version is available at http://www.asiaa.sinica.edu.tw/~okabe/files/a2163_WL_astroph.pd

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