Zobrazeno 1 - 10
of 91
pro vyhledávání: '"Paul, Debolina"'
The problem of linear predictions has been extensively studied for the past century under pretty generalized frameworks. Recent advances in the robust statistics literature allow us to analyze robust versions of classical linear models through the pr
Externí odkaz:
http://arxiv.org/abs/2201.01973
Recent advances in center-based clustering continue to improve upon the drawbacks of Lloyd's celebrated $k$-means algorithm over $60$ years after its introduction. Various methods seek to address poor local minima, sensitivity to outliers, and data t
Externí odkaz:
http://arxiv.org/abs/2110.14148
The concept of Entropy plays a key role in Information Theory, Statistics, and Machine Learning.This paper introduces a new entropy measure, called the t-entropy, which exploits the concavity of the inverse-tan function. We analytically show that the
Externí odkaz:
http://arxiv.org/abs/2105.00316
Principal Component Analysis (PCA) is a fundamental tool for data visualization, denoising, and dimensionality reduction. It is widely popular in Statistics, Machine Learning, Computer Vision, and related fields. However, PCA is well-known to fall pr
Externí odkaz:
http://arxiv.org/abs/2102.03403
Mean shift is a simple interactive procedure that gradually shifts data points towards the mode which denotes the highest density of data points in the region. Mean shift algorithms have been effectively used for data denoising, mode seeking, and fin
Externí odkaz:
http://arxiv.org/abs/2012.10929
Kernel $k$-means clustering is a powerful tool for unsupervised learning of non-linearly separable data. Since the earliest attempts, researchers have noted that such algorithms often become trapped by local minima arising from non-convexity of the u
Externí odkaz:
http://arxiv.org/abs/2011.06461
Publikováno v:
In Applied Surface Science 1 February 2025 681
Even with the rise in popularity of over-parameterized models, simple dimensionality reduction and clustering methods, such as PCA and k-means, are still routinely used in an amazing variety of settings. A primary reason is the combination of simplic
Externí odkaz:
http://arxiv.org/abs/2008.07110
Despite its well-known shortcomings, $k$-means remains one of the most widely used approaches to data clustering. Current research continues to tackle its flaws while attempting to preserve its simplicity. Recently, the \textit{power $k$-means} algor
Externí odkaz:
http://arxiv.org/abs/2001.03452
Autor:
Paul, Debolina1 (AUTHOR), Mane, Pratap2 (AUTHOR), Sarkar, Utpal1 (AUTHOR) utpalchemiitkgp@yahoo.com, Chakraborty, Brahmananda3,4 (AUTHOR) brahma@barc.gov.in
Publikováno v:
Theoretical Chemistry Accounts: Theory, Computation, & Modeling. Oct2023, Vol. 142 Issue 10, p1-11. 11p.