methodology-5

Supervised dimensionality reduction

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Adapting Text Embeddings for Causal Inference

Victor Veitch, Dhanya Sridhar, D. Blei•Tue May 28 2019

A method to estimate causal effects from observational text data, adjusting for confounding features of the text such as the subject or writing quality, and studies causally sufficient embeddings with semi-synthetic datasets and finds that they improve causal estimation over related embedding methods.

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Dimensionality Reduction Using Similarity-Induced Embeddings

Nikolaos Passalis, A. Tefas•Sat Jun 17 2017

A new DR framework that can directly model the target distribution using the notion of similarity instead of distance is introduced and it is demonstrated that it can outperform many existing DR techniques.

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Supervised dimensionality reduction for big data

J. Vogelstein, Eric W. Bridgeford, M. Tang, Da Zheng, C. Douville, R. Burns, M. Maggioni•Sun May 16 2021

This work introduces an approach to extending principal components analysis by incorporating class-conditional moment estimates into the low-dimensional projection, and proves that Linear Optimal Low-Rank Projection and its generalizations lead to improved data representations for subsequent classification, while maintaining computational efficiency and scalability.

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SRP: Efficient class-aware embedding learning for large-scale data via supervised random projections

Amir-Hossein Karimi, A. Wong, A. Ghodsi•Tue Nov 06 2018

Experimental results demonstrate that the proposed SRP strategy for class-aware embedding learning can be very promising in producing embeddings that are highly competitive with existing supervised dimensionality reduction methods while achieving 1-2 orders of magnitude better computational performance.

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Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction

Dongrui Wu, Zhenhua Shi, Jian Huang, Yu-kai Wang, Chin-Teng Lin•Wed Jan 08 2020

This work proposes a novel linear dimensionality reduction approach, supervised discriminative sparse PCA with adaptive neighbors (SDSPCAAN), to integrate neighborhood-free supervised discrim inative sparsePCA and projected clustering with adaptive neighbor and validated the effectiveness and robustness of this approach.

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Supervised dimensionality reduction by a Linear Discriminant Analysis on pre-trained CNN features

Francisco J. H. Heras, G. G. Polavieja•Sun Jun 21 2020

This work explores the application of linear discriminant analysis (LDA) to the features obtained in different layers of pretrained deep convolutional neural networks (CNNs) to find that the centroids of classes corresponding to the similar data lay closer thanclasses corresponding to different data.

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Stochastic Mutual Information Gradient Estimation for Dimensionality Reduction Networks

Ozan Ozdenizci, Deniz Erdoğmuş•Fri Apr 30 2021

This work presents a dimensionality reduction network (MMINet) training procedure based on the stochastic estimate of the mutual information gradient and introduces emerging information theoretic feature transformation protocols as an end-to-end neural network training approach.

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Computer-aided Interpretable Features for Leaf Image Classification

Jayani P. G. Lakshika, T. Talagala•Mon Jun 14 2021

This work explored the ability of features to discriminate the classes of interest under supervised learning and unsupervised learning settings, and introduced 52 computationally efficient features to classify plant species.

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Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOM

Adam Šmelko, Sona Molnárová, Miroslav Kratochvíl, Abhishek Koladiya, J. Musil, Martin Kruliš, J. Vondrášek•Sun Jan 02 2022

This paper describes an e-cient, highly parallel GPU implementation of EmbedSOM designed to provide interactive results on large datasets, and presents BlosSOM, a high-performance semi-supervised dimensionality reduction so-called for interactive user-steerable visualization of high-dimensional datasets with millions of individual data points.

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SLISEMAP: supervised dimensionality reduction through local explanations

Anton Björklund, J. Mäkelä, Kai Puolamäki•Tue Jan 11 2022

This work proposes a new supervised manifold visualisation method, slisemap, that simultaneously finds local explanations for all data items and builds a (typically) two-dimensional global visualisation of the black box model such that data items with similar local explanations are projected nearby.

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