arXiv subjects

Yazhou Yang

Publications and source records attributed to Yazhou Yang.

5 recordsLinked to original sources

arXiv2026

Composition and Space Weathering Characteristics of Tianwen-2 Mission's First Target Near-Earth Asteroid (469219) Kamo`oalewa

The near-Earth asteroid Kamo`oalewa, a quasi-satellite of the Earth and the target for sample return by China's Tianwen-2 mission, exhibits distinctive spectral characteristics. This study re-analyzes the visible and near-infrared reflectance spectrum of Kamo`oalewa published by B. N. L. Sharkey et al. (2021), obtained using the Large Binocular Telescope, to infer its mineral composition and space weathering characteristics. Spectral similarity analysis is performed by comparing the spectrum of Kamo`oalewa to the mean spectra of various types in the Bus-DeMeo taxonomy to make a preliminary constraint on the combined characteristics of surface mineralogy and space weathering effects. To further characterize the mineral composition, a detailed analysis of the 1 {\mu}m band center is conducted based on spectral data below 1.25 {\mu}m that have higher signal-to-noise ratios. Empirical models for normalized spectra are developed to estimate the Is/FeO content. The results suggest that asteroid Kamo`oalewa has higher olivine abundance than that of typical S-type asteroids and the Moon, exhibiting an immature to submature degree of space weathering. These findings enhance our understanding of the evolution of similar quasi-satellites and provide important implication for the future exploration of Tianwen-2 mission.

astro-ph.EP↗

arXiv2018

A Benchmark and Comparison of Active Learning for Logistic Regression

Logistic regression is by far the most widely used classifier in real-world applications. In this paper, we benchmark the state-of-the-art active learning methods for logistic regression and discuss and illustrate their underlying characteristics. Experiments are carried out on three synthetic datasets and 44 real-world datasets, providing insight into the behaviors of these active learning methods with respect to the area of the learning curve (which plots classification accuracy as a function of the number of queried examples) and their computational costs. Surprisingly, one of the earliest and simplest suggested active learning methods, i.e., uncertainty sampling, performs exceptionally well overall. Another remarkable finding is that random sampling, which is the rudimentary baseline to improve upon, is not overwhelmed by individual active learning techniques in many cases.

stat.ML↗

arXiv2018

Single Shot Active Learning using Pseudo Annotators

Standard myopic active learning assumes that human annotations are always obtainable whenever new samples are selected. This, however, is unrealistic in many real-world applications where human experts are not readily available at all times. In this paper, we consider the single shot setting: all the required samples should be chosen in a single shot and no human annotation can be exploited during the selection process. We propose a new method, Active Learning through Random Labeling (ALRL), which substitutes single human annotator for multiple, what we will refer to as, pseudo annotators. These pseudo annotators always provide uniform and random labels whenever new unlabeled samples are queried. This random labeling enables standard active learning algorithms to also exhibit the exploratory behavior needed for single shot active learning. The exploratory behavior is further enhanced by selecting the most representative sample via minimizing nearest neighbor distance between unlabeled samples and queried samples. Experiments on real-world datasets demonstrate that the proposed method outperforms several state-of-the-art approaches.

stat.ML↗

arXiv2018

A Variance Maximization Criterion for Active Learning

Active learning aims to train a classifier as fast as possible with as few labels as possible. The core element in virtually any active learning strategy is the criterion that measures the usefulness of the unlabeled data based on which new points to be labeled are picked. We propose a novel approach which we refer to as maximizing variance for active learning or MVAL for short. MVAL measures the value of unlabeled instances by evaluating the rate of change of output variables caused by changes in the next sample to be queried and its potential labelling. In a sense, this criterion measures how unstable the classifier's output is for the unlabeled data points under perturbations of the training data. MVAL maintains, what we refer to as, retraining information matrices to keep track of these output scores and exploits two kinds of variance to measure the informativeness and representativeness, respectively. By fusing these variances, MVAL is able to select the instances which are both informative and representative. We employ our technique both in combination with logistic regression and support vector machines and demonstrate that MVAL achieves state-of-the-art performance in experiments on a large number of standard benchmark datasets.

stat.ML↗

arXiv2017

Active Learning Using Uncertainty Information

Many active learning methods belong to the retraining-based approaches, which select one unlabeled instance, add it to the training set with its possible labels, retrain the classification model, and evaluate the criteria that we base our selection on. However, since the true label of the selected instance is unknown, these methods resort to calculating the average-case or worse-case performance with respect to the unknown label. In this paper, we propose a different method to solve this problem. In particular, our method aims to make use of the uncertainty information to enhance the performance of retraining-based models. We apply our method to two state-of-the-art algorithms and carry out extensive experiments on a wide variety of real-world datasets. The results clearly demonstrate the effectiveness of the proposed method and indicate it can reduce human labeling efforts in many real-life applications.

stat.ML↗