Journal Article10.1016/J.PATREC.2021.09.017
Weighted multi-view common subspace learning method
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TL;DR: Wang et al. as mentioned in this paper proposed a weighted common subspace learning method, which can effectively adjust the contribution ratio of between-class and within-class information through a weighted parameter, so that an optimized common subspaces can be obtained.
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About: This article is published in Pattern Recognition Letters. The article was published on 01 Nov 2021. The article focuses on the topics: Projection (set theory) & Subspace topology.
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References
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
30.8K
•Book
Pattern Recognition and Machine Learning
Christopher M. Bishop
- 17 Aug 2006
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
Peter N. Belhumeur,Joao P. Hespanha,David J. Kriegman +2 more
- 15 Apr 1996
TL;DR: A face recognition algorithm which is insensitive to gross variation in lighting direction and facial expression is developed and the proposed “Fisherface” method has error rates that are significantly lower than those of the Eigenface technique when tested on the same database.
Combining labeled and unlabeled data with co-training
Avrim Blum,Tom M. Mitchell +1 more
- 24 Jul 1998
TL;DR: A PAC-style analysis is provided for a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views, to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples.
6.4K