Journal ArticleDOI
Kernel Self-optimized Locality Preserving Discriminant Analysis for feature extraction and recognition
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TLDR
The comparative experiments show that KSLPDA outperforms PCA, LDA, LPP, supervised LPP and kernel supervised L PP on feature extraction for classification.About:
This article is published in Neurocomputing.The article was published on 2011-10-01. It has received 25 citations till now. The article focuses on the topics: Kernel Fisher discriminant analysis & Kernel principal component analysis.read more
Citations
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Journal ArticleDOI
Overview and comparative study of dimensionality reduction techniques for high dimensional data
TL;DR: This paper presents the state-of-the art dimensionality reduction techniques and their suitability for different types of data and application areas and the issues of dimensionality Reduction techniques that can affect the accuracy and relevance of results.
Journal ArticleDOI
Simultaneous feature weighting and parameter determination of Neural Networks using Ant Lion Optimization for the classification of breast cancer
TL;DR: A wrapper method utilizing the Ant Lion Optimization algorithm is presented that searches for best feature weights and parametric values of Multilayer Neural Network simultaneously, which validates the work which has the potential for becoming an alternative to the other well-known techniques.
Journal ArticleDOI
Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization
TL;DR: This paper trains support vector machines (SVM)-based localized multiple kernel learning (LMKL), using the alternating optimization between the standard SVM solvers with the local combination of base kernels and the sample-specific kernel weights, using a new primal-dual equivalence.
Journal ArticleDOI
Kernel Ridge Regression with Lagged-Dependent Variable: Applications to Prediction of Internal Bond Strength in a Medium Density Fiberboard Process
TL;DR: Experimental results show that the proposed approaches perform better than KRR or ridge regression and yield consistently better results than OLS with LDVs, implying that it can be used as a promising alternative when there are autocorrelations of response variables.
Journal ArticleDOI
Kernel self-optimization learning for kernel-based feature extraction and recognition
TL;DR: In this paper, a uniform framework for kernel self-optimization with the ability to adjust the data structure is presented, where the data-dependent kernel is extended and applied to kernel learning, and optimization equations with two criteria for measuring data discrimination are used to solve the optimal parameter values.
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
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.
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.
Journal ArticleDOI
Face recognition using Laplacianfaces
TL;DR: Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.