In this research work, we have developed a bio inspired PSO algorithm in combination with descriptive statistics equation based model. In this work, based on the mathematical relationship between the Entropy, Uniformity and the threshold measured in terms of intensity. It is found using box plot and curve fitting techniques to calculate automatic input threshold for PSO. The results show highly...

In this paper, projection statistics (PS) are applied to detect and mitigate outliers in adaptive processing algorithms. Outliers present in training data result in slow adaptive processor convergence. In radar, these outliers may arise from clutter discretes, desired targets, or jammers. PS provide a computationally tractable technique for identifying and mitigating outlier data samples prior ...

The IceCube project has transformed 1 km of deep natural Antarctic ice into a Cherenkov detector. Muon neutrinos are detected and their direction is inferred by mapping the light produced by the secondary muon track inside the volume instrumented with photomultipliers. Reconstructing the muon track from the observed light is challenging due to noise, light scattering in the ice medium, and the ...

One major question in molecular biology is whether the spatial distribution of observed molecules is random or organized in clusters. Indeed, this analysis gives information about molecules' interactions and physical interplay with their environment. The standard tool for analyzing molecules' distribution statistically is the Ripley's K function, which tests spatial randomness through the compu...

Many strategies for asset allocation involve the computation of expected returns and the covariance or correlation matrix of financial instruments returns. How much of each instrument to own is determined by an attempt to minimize risk (the variance of linear combinations of investments in these financial assets) subject to various constraints such as a given level of return, concentration limi...

We study statistical inference and robust solution methods for stochastic optimization prob-lems. We first develop an empirical likelihood framework for stochastic optimization. We showan empirical likelihood theory for Hadamard differentiable functionals with general f -divergencesand give conditions under which T (P ) = infx∈X EP [`(x; ξ)] is Hadamard differentiable. Noting<lb...

In this paper, we propose an acoustic model training technique which is robust against outliers such as clipping, unexpected noise, poorly pronounced word segments, or mistranscriptions, which deteriorate the quality of the acoustic models and in turn decrease speech recognition performance. The outlier-robust acoustic model training technique is based on a maximum likelihood (ML) criterion and...

Principal Component Analysis (PCA) is the problem of finding a lowrank approximation to a matrix. It is a central problem in statistics, but it is sensitive to sparse errors with large magnitudes. Robust PCA addresses this problem by decomposing a matrix into the sum of a low-rank matrix and a sparse matrix, thereby separating out the sparse errors. This paper provides a background in robust PC...

This paper unifies “line-process” approaches for regularization with discontinuities and robust estimation techniques. We generalize the notion of a “line process” to that of an analog “outlier process” and show that a problem formulated in terms of outlier processes can be viewed an terms of robust statistics. We also characterize a class of robust statistical problems for which an equivalent ...

A method for deriving optic ow, using robust statistics, is derived and applied to standard test image sequences. We compare the performance of our approach against the best results derived from competing methods. The method out-performs all published methods in terms of accuracy. The method is also cheaper to implement than most of the other methods. One of the key features in the success of t...