Globular cluster detection in the GAIA survey

Mohammadi, Mohammad; Petkov, Nicolai; Bunte, Kerstin; Peletier, Reynier F.; Schleif, Frank-Michael

doi:10.1016/j.neucom.2018.10.081

Cited by 6 publications

(10 citation statements)

References 19 publications

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“…Thereby, several seeding points are used and the shorted paths within the shape are calculated in contrast to the Euclidean distance between the landmarks. Further examples can be found in physics where problems of the special relativity theory or other research topics naturally lead to indefinite spaces [50].…”

Section: Indefinite Proximity Functionsmentioning

confidence: 99%

Data-Driven Supervised Learning for Life Science Data

Münch

Raab

Biehl

et al. 2020

Front. Appl. Math. Stat.

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Life science data are often encoded in a non-standard way by means of alphanumeric sequences, graph representations, numerical vectors of variable length, or other formats. Domain-specific or data-driven similarity measures like alignment functions have been employed with great success. The vast majority of more complex data analysis algorithms require fixed-length vectorial input data, asking for substantial preprocessing of life science data. Data-driven measures are widely ignored in favor of simple encodings. These preprocessing steps are not always easy to perform nor particularly effective, with a potential loss of information and interpretability. We present some strategies and concepts of how to employ data-driven similarity measures in the life science context and other complex biological systems. In particular, we show how to use data-driven similarity measures effectively in standard learning algorithms.

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Section: Indefinite Proximity Functionsmentioning

confidence: 99%

Data-Driven Supervised Learning for Life Science Data

Münch

Raab

Biehl

et al. 2020

Front. Appl. Math. Stat.

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“…After symmetrization P 1 " 1 2 pP `P T q, the spectral clustering can be implemented based on P 1 . From panels c and e, we see that the resulting clusters coincide with the three known groups called: Main Sequence (MS), Horizontal Branch (HB) and Red Giant Branch (RGB) (see (Mohammadi et al 2019)). It can be seen that MS is separable since it is a 2D manifold while HB and RGB are 1D manifolds.…”

Section: Manifold Alignment Aware Ants (M3a)mentioning

confidence: 71%

“…To extract stars of a GC, we use objects which are not further than 0.05 degree from the center of the GC. Then, we use their photometric information since it reveals at which stage of life a star is (for more details see (Mohammadi et al 2019)). Panels b and d display the clusters detected by spectral clustering using a Gaussian kernel (Von Luxburg 2007).…”

Section: Manifold Alignment Aware Ants (M3a)mentioning

confidence: 99%

“…We use the magnitude distributions of the stars contained in rectangular windows to detect globular clusters in the GAIA database. As proposed in (Tian 2017) and (Mohammadi et al 2018) we consider windows of 0.5 degree length in both right ascension (RA) and declination (Dec). The left panel in Fig.…”

Section: Data and Preprocessingmentioning

confidence: 99%

“…Here, we extend the original proposal in (Mohammadi et al 2018) to use the full position information. This can be achieved by using three dimensional KDE (Simonoff 2012).…”

Section: Preprocessingmentioning

confidence: 99%

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Novel techniques of computational intelligence for analysis of astronomical structures

Mohammadi¹

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The presence of manifolds is a common assumption in many applications including astronomy and computer vision. For instance, in astronomy low-dimensional stellar structures, such as streams, shells and globular clusters, can be found in the neighborhood of big galaxies such as the Milky Way. Since these structures are often buried in very large data sets, an algorithm which can not only recover the manifold, but also remove the background noise (or outliers) is highly desirable. While other works try to recover manifolds either by pushing all points towards manifolds or by down-sampling from dense regions, aiming to solve one of the problems, they generally fail to suppress the noise on manifolds and remove background noise simultaneously. Inspired by the collective behavior of biological ants in food-seeking process, we propose a new algorithm which employs several random walkers who are equipped with a local alignment measure to detect and denoise manifolds. During the walking process the agents release pheromone on data points which reinforces future movements. Over time the pheromone concentrates on the manifolds, while it fades in the background noise due to an evaporation procedure. We use the Markov Chain (MC) framework to provide a theoretical analysis of the convergence of the algorithm and its performance. Moreover, an empirical analysis, based on synthetic and real world data sets, is provided to demonstrate its applicability in different areas, such as: a) improving the performance of t-distributed stochastic neighbor embedding (t-SNE) and spectral clustering using the underlying MC formulas, b) recovering astronomical low-dimensional structures and c) improving the performance of the Fast Parzen Window density estimator.1 While the proposed MC in this chapter is non-homogeneous (because of changes in pheromone vector), the MC can still be consider homogeneous between two updates of the pheromone.

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