Journal ArticleDOI
Multiple kernel local Fisher discriminant analysis for face recognition
Ziqiang Wang,Xia Sun +1 more
TLDR
A new algorithm termed multiple kernel local Fisher discriminant analysis (MKLFDA) is proposed, which produces nonlinear discriminant features via kernel theory, and considers multiple image features with multiple base kernels.About:
This article is published in Signal Processing.The article was published on 2013-06-01. It has received 38 citations till now. The article focuses on the topics: Kernel Fisher discriminant analysis & Linear discriminant analysis.read more
Citations
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Journal ArticleDOI
Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms
TL;DR: The study highlights the feasibility of representing medical health records using the BoW for multi-label classification tasks and highlights that dimensionality reduction algorithms based on kernel methods, locality preserving projections or both are good candidates to deal with multi- label classification tasks in medical time series with many missing values and high label density.
Journal ArticleDOI
Locality Preserving Composite Kernel Feature Extraction for Multi-Source Geospatial Image Analysis
Yuhang Zhang,Saurabh Prasad +1 more
TL;DR: Experimental results show that composite kernel local Fisher's discriminant analysis when combined with MLR based classifier (CKLFDA-MLR) is very effective at feature extraction and classification of multi-source geospatial images.
Journal ArticleDOI
A hierarchical classification method using belief functions
TL;DR: A new supervised confidence-based classification method for multi-class problems using the belief function theory and feature selection, which has been tested for indoor localization in a wireless sensors network and for facial image recognition using well-known databases.
Journal ArticleDOI
A Two-Dimensional Framework of Multiple Kernel Subspace Learning for Recognizing Emotion in Speech
TL;DR: A two-dimensional framework for multiple kernel subspace learning that provides more linear combinations on the basis of MKL without nonnegative constraints and an algorithm, namely generalised multiple kernel discriminant analysis (GMKDA), by employing discriminant embedding graphs in this framework is proposed.
Journal ArticleDOI
Incremental supervised locally linear embedding for machinery fault diagnosis
TL;DR: In this paper, incremental supervised LLE (I-SLLE) is investigated for submersible plunger pump fault diagnosis and a new machinery fault diagnosis method is proposed based on I- SLLE.
References
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Journal ArticleDOI
Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Journal ArticleDOI
A global geometric framework for nonlinear dimensionality reduction.
TL;DR: An approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set and efficiently computes a globally optimal solution, and is guaranteed to converge asymptotically to the true structure.
Journal ArticleDOI
Laplacian Eigenmaps for dimensionality reduction and data representation
Mikhail Belkin,Partha Niyogi +1 more
TL;DR: In this article, the authors proposed a geometrically motivated algorithm for representing high-dimensional data, based on the correspondence between the graph Laplacian, the Laplace Beltrami operator on the manifold and the connections to the heat equation.
Proceedings Article
Locality Preserving Projections
Xiaofei He,Partha Niyogi +1 more
TL;DR: These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the data set by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold.