Sparse Modeling for Astronomical Data Analysis

Interactive Sparse astronomical data Analysis Packages: iSAP

Sparse Modeling for High - Dimensional Multi - Manifold Data Analysis

High-dimensional data are ubiquitous in many areas of science and engineering, such as machine learning, signal and image processing, computer vision, pattern recognition, bioinformatics, etc. Often, high-dimensional data are not distributed uniformly in the ambient space; instead they lie in or close to a union of low-dimensional manifolds. Recovering such low-dimensional structures in the dat...

modeling loss data by phase-type distribution

بیمه گران همیشه بابت خسارات بیمه نامه های تحت پوشش خود نگران بوده و روش هایی را جستجو می کنند که بتوانند داده های خسارات گذشته را با هدف اتخاذ یک تصمیم بهینه مدل بندی نمایند. در این پژوهش توزیع های فیزتایپ در مدل بندی داده های خسارات معرفی شده که شامل استنباط آماری مربوطه و استفاده از الگوریتم em در برآورد پارامترهای توزیع است. در پایان امکان استفاده از این توزیع در مدل بندی داده های گروه بندی ...

A safe data analysis environment for astronomical data centres

The volume of data available to astronomers via the Internet is doubling every twelve months or so. This is due both to the increase in the number of data sources (astronomy is an active field, while archives of old observations retain scientific value) and in the size of individual data sources (multi-TB datasets are the norm in the current generation of sky surveys). The new sky surveys are g...

Sparse Representation of Astronomical Images

Sparse representation of astronomical images is discussed. It is shown that a significant gain in sparsity is achieved when particular mixed dictionaries are used for approximating these types of images with greedy selection strategies. Experiments are conducted to confirm (i) the effectiveness at producing sparse representations and (ii) competitiveness, with respect to the time required to pr...