ABSTRACT We apply network Lasso to solve binary classification (clustering) problems on network structured data. To this end, we generalize ordinary logistic regression to non-Euclidean data defined over a complex network structure. A scalable classification algorithm is obtained by applying the alternating direction methods of multipliers to solve this optimization problem. Index Terms-- compressed sensing, big data over networks, semi-supervised learning, classification, clustering, complex networks, convex optimization I. INTRODUCTION We consider the problem of classifying or clustering a large set of data points which conform to an underlying network structure. Such network-structured datasets arise in a wide range of application domains, e.g., image-and video processing as well as social networks [1].

The Business Statistics and Analysis Specialization is designed to equip you with a basic understanding of business data analysis tools and techniques. You'll master essential spreadsheet functions, build descriptive business data measures, and develop your aptitude for data modeling. You'll also explore basic probability concepts, including measuring and modeling uncertainty, and you'll use various data distributions, along with the Linear Regression Model, to analyze and inform business decisions. The Specialization culminates with a Capstone Project in which you'll apply the skills and knowledge you've gained to an actual business problem. To successfully complete all course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later.

I recently created a'particle optimizer' and published a pip python package called kernelml. The motivation for making this algorithm was to give analysts and data scientists a generalized machine learning algorithm for complex loss functions and non-linear coefficients. The optimizer uses a combination of simple machine learning and probabilistic simulations to search for optimal parameters using a loss function, input and output matrices, and (optionally) a random sampler. I am currently working more features and hope to eventually make the project open source. Lets take the problem of clustering longitude and latitude coordinates.

Data Science is quite a large and diverse field. As a result, it is really difficult to be a jack of all trades. Traditionally, Data Science would focus on mathematics, computer science and domain expertise. While I will briefly cover some computer science fundamentals, the bulk of this blog will mostly cover the mathematical basics one might either need to brush up on (or even take an entire course). In most data science workplaces, software skills are a must. While I understand most of you reading this are more math heavy by nature, realize the bulk of data science (dare I say 80%) is collecting, cleaning and processing data into a useful form.

This module, along with module 2B introduces two key concepts in statistics/epidemiology, confounding and effect modification. A relation between an outcome and exposure of interested can be confounded if a another variable (or variables) is associated with both the outcome and the exposure. In such cases the crude outcome/exposure associate may over or under-estimate the association of interest. Confounding is an ever-present threat in non-randomized studies, but results of interest can be adjusted for potential confounders.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

As we know from the basic maths that if we plot an'X','Y' graph, a linear relationship will always come up with a straight line. The equation of a straight line is written using the y mx b, where m is the slope (Gradient) and b is y-intercept (where the line crosses the Y axis). Once we get the equation of a straight line from 2 points in space in y mx b format, we can use the same equation to predict the points at different values of x which result in a straight line. In this formula, m is the slope and b is y-intercept. Let's take a real world example to demonstrate the usage of linear regression and usage of Least Square Method to reduce the errors Let's take a real world example of the price of agricultural products and how it varies based on the location its sold.

Non-negative Matrix Factorization (NMF) is a popular clustering and dimension reduction method by decomposing a non-negative matrix into the product of two lower dimension matrices composed of basis vectors. In this paper, we propose a semi-orthogonal NMF method that enforces one of the matrices to be orthogonal with mixed signs, thereby guarantees the rank of the factorization. Our method preserves strict orthogonality by implementing the Cayley transformation to force the solution path to be exactly on the Stiefel manifold, as opposed to the approximated orthogonality solutions in existing literature. We apply a line search update scheme along with an SVD-based initialization which produces a rapid convergence of the algorithm compared to other existing approaches. In addition, we present formulations of our method to incorporate both continuous and binary design matrices. Through various simulation studies, we show that our model has an advantage over other NMF variations regarding the accuracy of the factorization, rate of convergence, and the degree of orthogonality while being computationally competitive. We also apply our method to a text-mining data on classifying triage notes, and show the effectiveness of our model in reducing classification error compared to the conventional bag-of-words model and other alternative matrix factorization approaches.

In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature extractor for clustering tasks. The trained embedding function transfers knowledge from the domain of a labeled set of morphologically-distinct images, known as classes, to a new domain within which new classes can potentially be isolated and identified. Our target application in this paper is the Gravity Spy Project, which is an effort to characterize transient, non-Gaussian noise present in data from the Advanced Laser Interferometer Gravitational-wave Observatory, or LIGO. Accumulating large, labeled sets of noise features and identifying of new classes of noise lead to a better understanding of their origin, which makes their removal from the data and/or detectors possible.

This paper proposes a new dimensionality reduction algorithm named branching embedding (BE). It converts a dendrogram to a two-dimensional scatter plot, and visualizes the inherent structures of the original high-dimensional data. Since the conversion part is not computationally demanding, the BE algorithm would be beneficial for the case where hierarchical clustering is already performed. Numerical experiments revealed that the outputs of the algorithm moderately preserve the original hierarchical structures.