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Manifold learning: What, how, and why
Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to
find the low-dimensional structure of data. Dimension reduction for large, high-dimensional …
find the low-dimensional structure of data. Dimension reduction for large, high-dimensional …
Accelerated hierarchical density based clustering
We present an accelerated algorithm for hierarchical density based clustering. Our new
algorithm improves upon HDBSCAN*, which itself provided a significant qualitative …
algorithm improves upon HDBSCAN*, which itself provided a significant qualitative …
[ספר][B] Frontiers in massive data analysis
National Research Council, Division on Engineering… - 2013 - books.google.com
Data mining of massive data sets is transforming the way we think about crisis response,
marketing, entertainment, cybersecurity and national intelligence. Collections of documents …
marketing, entertainment, cybersecurity and national intelligence. Collections of documents …
Maximum inner-product search using cone trees
The problem of efficiently finding the best match for a query in a given set with respect to the
Euclidean distance or the cosine similarity has been extensively studied. However, the …
Euclidean distance or the cosine similarity has been extensively studied. However, the …
Density estimation trees
In this paper we develop density estimation trees (DETs), the natural analog of classification
trees and regression trees, for the task of density estimation. We consider the estimation of a …
trees and regression trees, for the task of density estimation. We consider the estimation of a …
[ספר][B] Advances in machine learning and data mining for astronomy
MJ Way, JD Scargle, KM Ali, AN Srivastava - 2012 - api.taylorfrancis.com
Advances in Machine Learning and Data Mining for Astronomy Page 1 W ay, Scargle, Chapman
& Hall/CRC Data Mining and Knowledge Discovery Series Advances in Machine Learning …
& Hall/CRC Data Mining and Knowledge Discovery Series Advances in Machine Learning …
End-to-end differentiable clustering with associative memories
Clustering is a widely used unsupervised learning technique involving an intensive discrete
optimization problem. Associative Memory models or AMs are differentiable neural networks …
optimization problem. Associative Memory models or AMs are differentiable neural networks …
Conditional t-SNE: more informative t-SNE embeddings
Dimensionality reduction and manifold learning methods such as t-distributed stochastic
neighbor embedding (t-SNE) are frequently used to map high-dimensional data into a two …
neighbor embedding (t-SNE) are frequently used to map high-dimensional data into a two …
Fast euclidean minimum spanning tree: algorithm, analysis, and applications
The Euclidean Minimum Spanning Tree problem has applications in a wide range of fields,
and many efficient algorithms have been developed to solve it. We present a new, fast …
and many efficient algorithms have been developed to solve it. We present a new, fast …
[PDF][PDF] Using the mutual k-nearest neighbor graphs for semi-supervised classification on natural language data
The first step in graph-based semi-supervised classification is to construct a graph from input
data. While the k-nearest neighbor graphs have been the de facto standard method of graph …
data. While the k-nearest neighbor graphs have been the de facto standard method of graph …