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6.6. Random Projection
The sklearn.random_projection module implements a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional varianc......
Read more at Scikit-learn User Guide
Random Projection And Its Role In Data Science
Often in Data Science, it can be very difficult to work with features that are very high-dimensional. This is because data in high-dimensions cannot be analyzed by computers or humans because it is…
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Random Projection in Python
Dimension reduction is usually a must-to-do preprocessing when dealing with big data. One of the most widely used methods is Principal Component Analysis (PCA), but the major shortcoming of PCA is…
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Random Projection Neural Networks
Welcome to the Papers Implemented Series. In this series, I am going to implement some very interesting papers in pytorch and try to explain the paper’s content in simple terms.
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The Johnson-Lindenstrauss bound for embedding with random projections
The Johnson-Lindenstrauss bound for embedding with random projections The Johnson-Lindenstrauss lemma states that any high dimensional dataset can be randomly projected into a lower dimensional Euclid...
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Save Computation Time for Clustering using Random Projections
From time to time, you have to deal with datasets with a large number of features. Here you can learn how to reduce them in an easy way.
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An Analysis of Shingling and Random Projection Algorithms
In a previous article, we discussed sequence similarity through Levenshtein distance and evaluated that at O(m*n). As we look to apply this algorithm to data sets of a larger size and scale, it…
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Behind the scenes on the Fast Random Projection algorithm for generating graph embeddings
The vast majority of data science and machine learning models rely on creating a vector, or embedding, of your data. Some of these embeddings naturally create themselves. For example, for numerical…
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“Interesting” Projections — Where PCA Fails.
Most data scientists are familiar with principal components analysis (PCA) as an exploratory data analysis tool. A recap for the uninitiated: researchers often use PCA for dimensionality reduction in…...
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Back Projection
In this tutorial you will learn: Theory What is Back Projection? How does it work? Code C++ Java Python Explanation C++ Java Python C++ Java Python C++ Java Python C++ Java Python C++ Java Python C++ ...
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The World Map with Many Faces — Map Projections
Member-only story The World Map with Many Faces — Map Projections Milan Janosov · Follow Published in Towards Data Science · 5 min read · Oct 1 -- 1 Share In this short piece, I review what map projec...
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Random
The Random class is present in the java.util package. It is used to generate random values or streams of random values of specific data types. Usage The Random class can be accessed by importing it as...
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Random
C++ has a std::rand() function from cstdlib library that generates a random number. For example, if we add include
Inverse Projection Transformation
When an image of a scene is captured by a camera, we lose depth information as objects and points in 3D space are mapped onto a 2D image plane. This is also known as a projective transformation, in…
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Self-Supervised Learning Using Projection Heads.
Boost performance with unlabeled data. Continue reading on Towards Data Science
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Random
The rand function will return a random number greater than or equal to 0 and less than the integer passed to the function. If an argument is not passed to the function, the return value is a random nu...
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Probabilistic View of Principal Component Analysis
Latent Variables, Expectation-Maximization & Variational Inference Continue reading on Towards Data Science
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Probability for machine learning
How is probability used in machine learning? Continue reading on Towards Data Science
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The Unpredictable “K Nearest Neighbour”
K nearest neighbour is one of the simplest algorithm in Machine Learning, Literally ,you just need to see it once through Geometry and that’s it, you wont forget it. In this series of articles we…
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Probabilities in Machine Learning| Part 2:
Part 1 of this series talked about Naive Bayes Classifier, that sorted discrete categorical data into probabilities to make predictions. In part 2, I will talk more about Gaussian Naive Bayes…
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Similarity Search, Part 6: Random Projections with LSH Forest
Understand how to hash data and reflect its similarity by constructing random hyperplanes Similarity search is a problem where given a query the goal is to find the most similar documents to it among...
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Estimate pi using random numbers
Okay, so I really should be publishing this on pi day (March 14th) but I don’t think that will happen so here it is anyway. Let’s estimate pi using random numbers in Julia — of course 😉. This…
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An illusion- Image Projection with OpenCV
Hey pals, how about having a cool project? Why not play around with the OpenCV (Open Source Computer Vision), a library used for computer vision? We are going to do the same now. With OpenCV, we are…
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An Introduction to Probability and the World of Chance
The concept of probability touches practically all aspects from our lives. In fact, we base many of the decisions we make on the probability of events we perceive to be the most beneficial…
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