Journal ArticleDOI
Locality-Based Discriminant Neighborhood Embedding
Jianping Gou,Zhang Yi +1 more
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TLDR
Experimental results show that the linear supervised subspace learning method called LDNE can be an effective and robust method for classification and compare it with the state-of-the-art dimensionality reduction techniques such as LPP and DNE on publicly available datasets.Abstract:
In this article, we develop a linear supervised subspace learning method called locality-based discriminant neighborhood embedding (LDNE), which can take advantage of the underlying submanifold-based structures of the data for classification. Our LDNE method can simultaneously consider both ‘locality’ of locality preserving projection (LPP) and ‘discrimination’ of discriminant neighborhood embedding (DNE) in manifold learning. It can find an embedding that not only preserveslocalinformationtoexploretheintrinsicsubmanifoldstructureofdatafromthesameclass, but also enhances the discrimination among submanifolds from different classes. To investigate the performance of LDNE, we compare it with the state-of-the-art dimensionality reduction techniques such as LPP and DNE on publicly available datasets. Experimental results show that our LDNE can be an effective and robust method for classification.read more
Citations
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Journal ArticleDOI
Maximum neighborhood margin discriminant projection for classification
TL;DR: A novel maximum neighborhood margin discriminant projection technique for dimensionality reduction of high-dimensional data that cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes.
Journal ArticleDOI
Discriminative globality and locality preserving graph embedding for dimensionality reduction
TL;DR: This article proposes a novel graph-based dimensionality reduction method entitled discriminative globality and locality preserving graph embedding (DGLPGE) by designing the informative globalities and locality preserves graph constructions.
Journal ArticleDOI
Sparsity and Geometry Preserving Graph Embedding for Dimensionality Reduction
TL;DR: A novel discriminative dimensionality reduction technique entitled sparsity and geometry preserving graph embedding (SGPGE), which can not only capture the sparse reconstructive relationships among training samples but also discover the intrinsic geometry and latent discrimination from high-dimensional data.
Journal ArticleDOI
Double graphs-based discriminant projections for dimensionality reduction
TL;DR: A novel graph embedding method named the double graphs-based discriminant projections (DGDP) by integrating two designed discriminative global graph constructions that outperforms the competing methods with more power of data representation and pattern discrimination in the embedded subspace.
Journal ArticleDOI
Combing K-means Clustering and Local Weighted Maximum Discriminant Projections for Weed Species Recognition
Shanwen Zhang,Jing Guo,Zhen Wang +2 more
TL;DR: The experimental results on the dataset of the weed species images show that the proposed method is effective for weed identification species, and can preliminarily meet the requirements of multi-row spraying of crop based on machine vision.
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.
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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.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.