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Polynomial linear discriminant analysis
The traditional linear discriminant analysis (LDA) is a classical dimensionality reduction method. But there are two problems with LDA. One is the...
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Regression Analysis in R: Linear Regression and Logistic Regression
This first chapter of the series of statistical data analysis using R, which the authors provides in this second part (PART II) of the book,...
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Linear Discriminant Analysis
Please download the sample Excel files from for this chapter’s exercises.
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Interpretable linear dimensionality reduction based on bias-variance analysis
One of the central issues of several machine learning applications on real data is the choice of the input features. Ideally, the designer should...
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Comparative Analysis of the Linear Regions in ReLU and LeakyReLU Networks
Networks with piecewise linear activation functions partition the input space into numerous linear regions. As such, the number of linear regions can...
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Capped norm linear discriminant analysis and its applications
Classical linear discriminant analysis (LDA) is based on squared Frobenious norm and hence is sensitive to outliers and noise. To improve the...
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qRLS: quantum relaxation for linear systems in finite element analysis
Quantum linear system algorithms (QLSAs) for gate-based quantum computing can provide exponential speedups for solving linear systems but face...
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Linear Algebra
This chapter provides an essential introduction to linear algebra, tailored to improve understanding of its importance in machine learning. It begins...
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A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis
High-Dimensional and Incomplete (HDI) data is commonly encountered in big data-related applications like social network services systems, which are...
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Linear Discrimination Analysis Using Image Processing Optimization
When we talk about Machinery Vision and Deep Learning, we often talk about algorithms. In fact, mathematical models with computer knowledge are the...
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Multiple Linear Regression Based Analysis of Weather Data: Assumptions and Limitations
Multiple linear regression is a statistical technique that is widely used in many fields, including weather forecasting. The primary aim of this...
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Analysis and computation of multidimensional linear complexity of periodic arrays
Linear complexity is an important parameter for arrays that are used in applications related to information security. In this work we survey...
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An Efficient Data Analysis Method for Big Data Using Multiple-Model Linear Regression
This paper introduces a new data analysis method for big data using a newly defined regression model named multiple model linear regression(MMLR),...
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Linear Models
Linear models are an important class of machine learning models that have been applied in various areas including genomic classification,...
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Low-rank approximation-based bidirectional linear discriminant analysis for image data
Dimensionality reduction methods for images directly without matrix-to-vector conversion have been widely concerned and achieved good classification...
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Attack Time Analysis in Dynamic Attack Trees via Integer Linear Programming
Attack trees (ATs) are an important tool in security analysis, and an important part of AT analysis is computing metrics. However, metric computation...
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The APC Algorithm of Solving Large-Scale Linear Systems: A Generalized Analysis
A new algorithm called accelerated projection-based consensus (APC) has recently emerged as a promising approach to solve large-scale systems of...
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Stability analysis and stabilization of semi-Markov jump linear systems with unavailable sojourn-time information
This study addresses the stability and stabilization problems of discrete-time semi-Markov jump linear systems (S-MJLSs) with unavailable...
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Deep Linear Discriminant Analysis with Variation for Polycystic Ovary Syndrome Classification
The polycystic ovary syndrome diagnosis is a problem that can be leveraged using prognostication based learning procedures. Many implementations of...