Related papers: Exploring Dimensionality Reduction of SDSS Spectra…

Electron, optical, and scanning probe microscopy methods are generating ever increasing volume of image data containing information on atomic and mesoscale structures and functionalities. This necessitates the development of the machine…

Scientific archives now contain hundreds of petabytes of data across genomics, ecology, climate, and molecular biology that could reveal undiscovered patterns if systematically analyzed at scale. Large-scale, weakly-supervised datasets in…

We are posting this 10-year-old white paper to support an upcoming survey description paper for the SDSS-III Apache Point Galactic Evolution Experiment (APOGEE) led by PI Dr. Steven Majewski. The white paper presented here was a…

Sky survey is one of the most important motivations to improve the astrophysics development, especially when using new photometric bands. We are performing the SAGE (Stellar Abundance and Galactic Evolution) survey with a self-designed SAGE…

High-dimensional image data often require dimensionality reduction before further analysis. This paper provides a purely analytical comparison of two linear techniques-Principal Component Analysis (PCA) and Singular Value Decomposition…

Aims. This paper introduces LRPayne, a novel algorithm designed for the efficient determination of stellar parameters and chemical abundances from low-resolution optical spectra, with a primary focus on data from large-scale galactic…

Hyperspectral image analysis often requires selecting the most informative bands instead of processing the whole data without losing the key information. Existing band reduction (BR) methods have the capability to reveal the nonlinear…

The study of galaxy evolution hinges on our ability to interpret multi-wavelength galaxy observations in terms of their physical properties. To do this, we rely on spectral energy distribution (SED) models which allow us to infer physical…

With the hyperspectral imaging technology, hyperspectral data provides abundant spectral information and plays a more important role in geological survey, vegetation analysis and military reconnaissance. Different from normal change…

Stationary subspace analysis (SSA) is a blind source separation framework that decomposes linearly mixed multivariate data into stationary and nonstationary components. We extend SSA to spatially indexed data by introducing spatial…

We present new elemental abundance studies of seven damped Lyman-alpha systems (DLAs). Together with the four DLAs analyzed in Dessauges-Zavadsky et al. (2004), we have a sample of eleven DLA galaxies with uniquely comprehensive and…

We apply machine learning in the form of a nearest neighbor instance-based algorithm (NN) to generate full photometric redshift probability density functions (PDFs) for objects in the Fifth Data Release of the Sloan Digital Sky Survey (SDSS…

Context. We have developed deep learning (DL) and AI-based tools to search extant narrow-band wide-field H$\alpha$ surveys of the Galactic Plane for elusive planetary nebulae (PNe) which are hidden in dense star fields towards the Galactic…

We study the dynamics of an online algorithm for learning a sparse leading eigenvector from samples generated from a spiked covariance model. This algorithm combines the classical Oja's method for online PCA with an element-wise…

Grouping stars by chemical similarity has the potential to reveal the Milky Way's evolutionary history. The APOGEE stellar spectroscopic survey has the resolution and sensitivity for this task. However, APOGEE lacks access to strong lines…

Discrete latent bottlenecks in variational autoencoders (VAEs) offer high bit efficiency and can be modeled with autoregressive discrete distributions, enabling parameter-efficient multimodal search with transformers. However, discrete…

The fundamental stellar atmospheric parameters T_eff and log g and 13 chemical abundances are derived for medium-resolution spectroscopy from LAMOST Medium-Resolution Survey (MRS) data sets with a deep-learning method. The neural networks…

Is there really much more to say about sparse autoencoders (SAEs)? Autoencoders in general, and SAEs in particular, represent deep architectures that are capable of modeling low-dimensional latent structure in data. Such structure could…

Principal Component Analysis (PCA) is a powerful and popular dimensionality reduction technique. However, due to its linear nature, it often fails to capture the complex underlying structure of real-world data. While Kernel PCA (kPCA)…

Studies of the degrees of freedom or "synergies" in musculoskeletal systems rely critically on algorithms to estimate the "dimension" of kinematic or neural data. Linear algorithms such as principal component analysis (PCA) are used almost…