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Biclustering, the process of simultaneously clustering the rows and columns of a data matrix, is a popular and effective tool for finding structure in a high-dimensional dataset. Many biclustering procedures appear to work well in practice,…

Methodology · Statistics 2020-06-04 Cheryl J. Flynn , Patrick O. Perry

Microarray techniques are widely used in Gene expression analysis. These techniques are based on discovering submatrices of genes that share similar expression patterns across a set of experimental conditions with coherence constraint.…

Quantitative Methods · Quantitative Biology 2020-01-27 Bilal Taher , Muhammad. H Fares , Saeed Jalili

This article introduces a general statistical modeling principle called "Density Sharpening" and applies it to the analysis of discrete count data. The underlying foundation is based on a new theory of nonparametric approximation and…

Methodology · Statistics 2021-08-24 Subhadeep Mukhopadhyay

A discretization of a continuum theory with constraints or conserved quantities is called mimetic if it mirrors the conserved laws or constraints of the continuum theory at the discrete level. Such discretizations have been found useful in…

General Relativity and Quantum Cosmology · Physics 2007-05-23 Cayetano Di Bartolo , Rodolfo Gambini , Jorge Pullin

Data Mining is the process of examining the information from different point of view and compressing it for the relevant data. This data can also be utilized to build the incomes. Data Mining is also known as Data or Knowledge Discovery.…

Databases · Computer Science 2016-10-17 Kratika Tyagi , Prof. Sanjeev Thakur

Traditionally, quantization is designed to minimize the reconstruction error of a data source. When considering downstream classification tasks, other measures of distortion can be of interest; such as the 0-1 classification loss.…

Machine Learning · Computer Science 2021-07-22 Daniel Severo , Elad Domanovitz , Ashish Khisti

This paper investigates two fundamental descriptors of data, i.e., density distribution versus mass distribution, in the context of clustering. Density distribution has been the de facto descriptor of data distribution since the…

Machine Learning · Statistics 2026-01-26 Kai Ming Ting , Ye Zhu , Hang Zhang , Tianrun Liang

Causal inference is essential for data-driven decision-making, as it aims to uncover causal relationships from observational data. However, identifying causality remains challenging due to the potential for confounding and the distinction…

Machine Learning · Statistics 2026-05-08 Tianyu Yang , Md. Noor-E-Alam

Clustering is a technique for the analysis of datasets obtained by empirical studies in several disciplines with a major application for biomedical research. Essentially, clustering algorithms are executed by machines aiming at finding…

Quantitative Methods · Quantitative Biology 2024-09-30 Diego Ulisse Pizzagalli , Santiago Fernandez Gonzalez , Rolf Krause

Recently there has been an increase in the studies on time-series data mining specifically time-series clustering due to the vast existence of time-series in various domains. The large volume of data in the form of time-series makes it…

Machine Learning · Computer Science 2019-12-06 Hossein Kamalzadeh , Abbas Ahmadi , Saeed Mansour

When dealing with continuous numeric features, we usually adopt feature discretization. In this work, to find the best way to conduct feature discretization, we present some theoretical analysis, in which we focus on analyzing correctness…

Machine Learning · Computer Science 2020-04-28 Qiang Liu , Zhaocheng Liu , Haoli Zhang

Clinical time series data are critical for patient monitoring and predictive modeling. These time series are typically multivariate and often comprise hundreds of heterogeneous features from different data sources. The grouping of features…

Machine Learning · Computer Science 2025-11-12 Fedor Sergeev , Manuel Burger , Polina Leshetkina , Vincent Fortuin , Gunnar Rätsch , Rita Kuznetsova

In this work, a new indexing technique of data streams called BSTree is proposed. This technique uses the method of data discretization, SAX [4], to reduce online the dimensionality of data streams. It draws on Btree to build the index and…

Databases · Computer Science 2014-06-24 Abdelwaheb Ferchichi , Mohamed Salah Gouider

Network binarization emerges as one of the most promising compression approaches offering extraordinary computation and memory savings by minimizing the bit-width. However, recent research has shown that applying existing binarization…

Computer Vision and Pattern Recognition · Computer Science 2023-05-23 Haotong Qin , Mingyuan Zhang , Yifu Ding , Aoyu Li , Zhongang Cai , Ziwei Liu , Fisher Yu , Xianglong Liu

Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying…

Machine Learning · Computer Science 2017-10-31 Jan Kukačka , Vladimir Golkov , Daniel Cremers

This survey addresses sampling discretization and its connections with other areas of mathematics. The survey concentrates on sampling discretization of norms of elements of finite-dimensional subspaces. We present here known results on…

Functional Analysis · Mathematics 2022-02-11 B. Kashin , E. Kosov , I. Limonova , V. Temlyakov

A class of causal effect functionals requires integration over conditional densities of continuous variables, as in mediation effects and nonparametric identification in causal graphical models. Estimating such densities and evaluating the…

Methodology · Statistics 2026-02-26 Xiaxian Ou , Razieh Nabi

Data generalization is a powerful technique for sanitizing multi-attribute data for publication. In a multidimensional model, a subset of attributes called the quasi-identifiers (QI) are used to define the space and a generalization scheme…

Databases · Computer Science 2021-08-12 Bijit Hore , Ravi Jammalamadaka , Sharad Mehrotra , Amedeo D'Ascanio

Diffusion models (DMs) have been significantly developed and widely used in various applications due to their excellent generative qualities. However, the expensive computation and massive parameters of DMs hinder their practical use in…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Xingyu Zheng , Xianglong Liu , Yichen Bian , Xudong Ma , Yulun Zhang , Jiakai Wang , Jinyang Guo , Haotong Qin

Dataset distillation, a training-aware data compression technique, has recently attracted increasing attention as an effective tool for mitigating costs of optimization and data storage. However, progress remains largely empirical.…

Machine Learning · Computer Science 2026-03-31 Yuri Kinoshita , Naoki Nishikawa , Taro Toyoizumi
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