354 search results for "pCA"

R benchmark for High-Performance Analytics and Computing (I)

April 14, 2016
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R benchmark for High-Performance Analytics and Computing (I)

  Objectives of Experiments R is more and more popular in various fields, including the high-performance analytics and computing (HPAC) fields. Nowadays, the architecture of HPC system can be classified as pure CPU system, CPU + Accelerators (GPGPU/FPGA) heterogeneous system, CPU + Coprocessors system. In software side, high performance scientific libraries, such as basic linear

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Perform co-operations with the coop package

April 6, 2016
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About The coop package does co-operations: covariance, correlation, and cosine, and it does them quickly. The package is available on CRAN and GitHub, and has two vignettes: Introducing coop: Fast Covariance, Correlation, and Cosine Operations Algorithms and Benchmarks for the coop Package Incidentally, the vignettes don't render correctly on CRAN's end for some reason; if any of you rmarkdown...

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Are you doing parallel computations in R? Then use BiocParallel

March 6, 2016
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Are you doing parallel computations in R? Then use BiocParallel

It’s the morning of the first day of oral conferences at #ENAR2016. I feel like I have a spidey sense since I woke up 3 min after an email from Jeff Leek; just a funny coincidence. Anyhow, I promised Valerie Obenchain at #Bioc2014 that I would write a post about one of my favorite Bioconductor packages:...

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Nairobi Data Science Meetup: Paradigm Shift in Research with Samuel Kamande

March 1, 2016
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Samuel Kamande is a Data Scientist at Nielsen and his presentation will focus on “Paradigm Shift in Research”. We caught up with him and he shared a lot about his work at Nielsen, some of the projects he has worked on like “Digital Divide project in Trinidad and Tobago in 2013”,thoughts on the future of Data Science and something...

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Principal Component Analysis using R

February 27, 2016
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Principal Component Analysis using R

Curse of Dimensionality:One of the most commonly faced problems while dealing with data analytics problem such as recommendation engines, text analytics is high-dimensional and sparse data. At many times, we face a situation where we have a large set of features and fewer data points, or we have data with very high feature vectors. In such scenarios,...

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Nairobi Data Science Meet Up:Finding deep structures in data with Chris Orwa

February 22, 2016
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I sat down with former rugby school captain whose rugby career was cut short by a shoulder injury while playing for Black Blad at Kenyatta University. It is always a great pleasure to talk to someone who is extremely passionate about what he does and his passion for Data Science was evident during my chat with “BlackOrwa” at iHub...

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Large scale eigenvalue decomposition and SVD with rARPACK

February 21, 2016
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In January 2016, I was honored to receive an “Honorable Mention” of the John Chambers Award 2016. This article was written for R-bloggers, whose builder, Tal Galili, kindly invited me to write an introduction to the rARPACK package. A Short Story of rARPACK Eigenvalue decomposition is a commonly used technique in numerous statistical problems. For example, principal component analysis (PCA) basically conducts eigenvalue...

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Clustering French Cities (based on Temperatures)

February 11, 2016
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Clustering French Cities (based on Temperatures)

In order to illustrate hierarchical clustering techniques and k-means, I did borrow François Husson‘s dataset, with monthly average temperature in several French cities. > temp=read.table( + "http://freakonometrics.free.fr/FR_temp.txt", + header=TRUE,dec=",") We have 15 cities, with monthly observations > X=temp > boxplot(X) Since the variance seems to be rather stable, we will not ‘normalize’ the variables here, > apply(X,2,sd) Janv Fevr Mars...

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Clusters of Texts

February 10, 2016
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Clusters of Texts

Another popular application of classification techniques is on texmining (see e.g. an old post on French president speaches). Consider the following example,  inspired by Nobert Ryciak’s post, with 12 wikipedia pages, on various topics, > library(tm) > library(stringi) > library(proxy) > titles = c("Boosting_(machine_learning)", + "Random_forest", + "K-nearest_neighbors_algorithm", + "Logistic_regression", + "Boston_Bruins", + "Los_Angeles_Lakers", + "Game_of_Thrones", + "House_of_Cards_(U.S._TV_series)", + "True Detective...

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Introduction to Statistical Methods in R

January 18, 2016
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Data analyses are the product of many different tasks, and statistical methods are one key aspect of any data analysis. There is a common workflow in the related areas of informatics, data mining, data science, machine learning, and statistics. The workflow tasks include data preparation, the development of predictive mathematical models, and the interpretation and Read More ...The...

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