Relevant bibliographies by topics / Fisher embedding regression

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  1. Journal articles
  2. Dissertations / Theses

Academic literature on the topic 'Fisher embedding regression'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Fisher embedding regression"

1

Djerrab, Moussab, Alexandre Garcia, Maxime Sangnier, and Florence d’Alché-Buc. "Output Fisher embedding regression." Machine Learning 107, no. 8-10 (2018): 1229–56. http://dx.doi.org/10.1007/s10994-018-5698-0.

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2

Kim, Taejoon, and Haiyan Wang. "Global Dense Vector Representations for Words or Items Using Shared Parameter Alternating Tweedie Model." Mathematics 13, no. 4 (2025): 612. https://doi.org/10.3390/math13040612.

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In this article, we present a model for analyzing the co-occurrence count data derived from practical fields such as user–item or item–item data from online shopping platforms and co-occurring word–word pairs in sequences of texts. Such data contain important information for developing recommender systems or studying the relevance of items or words from non-numerical sources. Different from traditional regression models, there are no observations for covariates. Additionally, the co-occurrence matrix is typically of such high dimension that it does not fit into a computer’s memory for modeling
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3

Xu, Hui, Yongguo Yang, Xin Wang, Mingming Liu, Hongxia Xie, and Chujiao Wang. "Multiple Kernel Dimensionality Reduction via Ratio-Trace and Marginal Fisher Analysis." Mathematical Problems in Engineering 2019 (January 14, 2019): 1–8. http://dx.doi.org/10.1155/2019/6941475.

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Traditional supervised multiple kernel learning (MKL) for dimensionality reduction is generally an extension of kernel discriminant analysis (KDA), which has some restrictive assumptions. In addition, they generally are based on graph embedding framework. A more general multiple kernel-based dimensionality reduction algorithm, called multiple kernel marginal Fisher analysis (MKL-MFA), is presented for supervised nonlinear dimensionality reduction combined with ratio-race optimization problem. MKL-MFA aims at relaxing the restrictive assumption that the data of each class is of a Gaussian distr
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4

Ou, Depin, Kun Tan, Qian Du, Jishuai Zhu, Xue Wang, and Yu Chen. "A Novel Tri-Training Technique for the Semi-Supervised Classification of Hyperspectral Images Based on Regularized Local Discriminant Embedding Feature Extraction." Remote Sensing 11, no. 6 (2019): 654. http://dx.doi.org/10.3390/rs11060654.

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This paper introduces a novel semi-supervised tri-training classification algorithm based on regularized local discriminant embedding (RLDE) for hyperspectral imagery. In this algorithm, the RLDE method is used for optimal feature information extraction, to solve the problems of singular values and over-fitting, which are the main problems in the local discriminant embedding (LDE) and local Fisher discriminant analysis (LFDA) methods. An active learning method is then used to select the most useful and informative samples from the candidate set. In the experiments undertaken in this study, the
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Dissertations / Theses on the topic "Fisher embedding regression"

1

Djerrab, Mousâab. "Supervised learning with output embeddings : contributions to learning with a handful of data and to structured prediction." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT018.

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Abstract:
La prédiction de variables de sortie non-vectorielles, suscite beaucoup d’intérêt en apprentissage statistique. Elle soulève principalement deux difficultés : la prise en compte de la structure explicite ou implicite de ces variables afin de faciliter la phase d’apprentissage, et le manque de données étiquetées, du fait du très grand nombre de valeurs que peuvent prendre ces variables et du coût de l’annotation. Pour résoudre le premier point, comme le deuxième, nous considérons dans cette thèse l’utilisation d’une fonction de redescription des sorties qui permet de plonger celles-ci dans un e
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