The t-distributed stochastic neighbor embedding (t- SNE) is a method for interpreting high dimensional (HD) data by mapping each point to a low dimensional (LD) space (usually two-dimensional). It seeks to retain the structure of the data. An important component of the t-SNE algorithm is the initialization procedure, which begins with the random initialization of an LD vector. Points in this initial vector are then updated to minimize the loss function (the KL divergence) iteratively using gradient descent. This leads comparable points to attract one another while pushing dissimilar points apart. We believe that, by default, these algorithms should employ some form of informative initialization. Another essential component of the t-SNE is using a kernel matrix, a similarity matrix comprising the pairwise distances among the sequences. For t-SNE-based visualization, the Gaussian kernel is employed by default in the literature. However, we show that kernel selection can also play a crucial role in the performance of t-SNE. In this work, we assess the performance of t-SNE with various alternative initialization methods and kernels, using four different sets, out of which three are biological sequences (nucleotide, protein, etc.) datasets obtained from various sources, such as the well-known GISAID database for sequences of the SARS- CoV-2 virus. We perform subjective and objective assessments of these alternatives. We use the resulting t-SNE plots and k- ary neighborhood agreement (k-ANA) to evaluate and compare the proposed methods with the baselines. We show that by using different techniques, such as informed initialization and kernel matrix selection, that t-SNE performs significantly better. Moreover, we show that t-SNE also takes fewer iterations to converge faster with more intelligent initialization.
Default Machine Learning Hyperparameters Do Not Provide Informative Initialization for Bayesian Optimization
Department of Telematics and Computing, Universidad Pontificia Comillas（电信与计算系，Pontificia Comillas大学）；Institute for Research in Technology (IIT), Universidad Pontificia Comillas（技术研究 institute，Pontificia Comillas大学）
HeFS: Helper-Enhanced Feature Selection via Pareto-Optimized Genetic Search
Jilin University（吉林大学）；Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（吉林大学符号计算与知识工程教育部重点实验室）；The Hong Kong University of Science and Technology（香港科技大学）；The Second People's Hospital of Changzhou, the Third Affiliated Hospital of Nanjing Medical University（常州市第二人民医院，南京医科大学第三附属医院）；Beijing Life Science Academy（北京生命科学研究院）
The Role of the Time-Dependent Hessian in High-Dimensional Optimization
1 Laboratoire de Physique de l'Ecole Normale Sup\'erieure, ENS, Universit\'e PSL, CNRS, Sorbonne Universit\'e, Universit\'e Paris Cit\'e, F-75005 Paris, France.
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization
Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）；Rhode Island Hospital（罗德岛医院）；Perelman School of Medicine at the University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；Second Xiangya Hospital of Central South University（中南大学湘雅二医院）；University of Virginia（弗吉尼亚大学）；Johns Hopkins University（约翰霍普金斯大学）；Central South University（中南大学）；Brown University（布朗大学）
