Nonparametric discriminant analysis and nearest neighbor classification

Bressan, Marco; Vitrià, Jordi

doi:10.1016/s0167-8655(03)00117-x

Cited by 100 publications

(67 citation statements)

References 9 publications

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“…linear and supervised, namely LDA, MFA, LSDA, SLPP, NDA, NCA and LMNN all of them with and without a PCA preprocessing. For LDA our own implementation was used, however for the rest, we used freely available implementations from the authors of Cai et al (2007); Bressan and Vitrià (2003); Weinberger et al (2006);Fowlkes et al (2007). For each of the baseline methods, the corresponding algorithm parameters were properly adjusted, and only the best result obtained in each case is shown.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore this work is related with other methods in which the optimization is based on trying to minimize the k-NN classification error probability, among them Nonparametric Discriminant Analysis (NDA) Bressan and Vitrià (2003), Neighbourhood Component Analysis (NCA) Goldberger et al (2005) and Large Margin Nearest Neighbour (LMNN) Weinberger et al (2006) are worth mentioning.…”

Section: Introductionmentioning

confidence: 99%

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Dimensionality reduction by minimizing nearest-neighbor classification error

Villegas

Paredes

2011

Pattern Recognition Letters

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There is a great interest in dimensionality reduction techniques for tackling the problem of high-dimensional pattern classification. This paper addresses the topic of supervised learning of a linear dimension reduction mapping suitable for classification problems. The proposed optimization procedure is based on minimizing an estimation of the nearest neighbor classifier error probability, and it learns a linear projection and a small set of prototypes that support the class boundaries. The learned classifier has the property of being very computationally efficient, making the classification much faster than state-of-the-art classifiers, such as SVMs, while having competitive recognition accuracy. The approach has been assessed through a series of experiments, showing a uniformly good behavior, and competitive compared with some recently proposed supervised dimensionality reduction techniques.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dimensionality reduction by minimizing nearest-neighbor classification error

Villegas

Paredes

2011

Pattern Recognition Letters

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“…We use PCA to extract 500 features from each data set, and then a discriminant analysis step is performed to obtain the 200 final features from each example. The NDA algorithm has been used for this purpose, which has been shown to improve the performance of other classic discriminant analysis techniques (Bressan and Vitria, 2003) under the NN rule. The new classes are added by projecting the training vectors on the reduced space, and using this projected features as a model for the new classification task.…”

Section: Methodsmentioning

confidence: 99%

MULTITASK LEARNING - An Application to Incremental Face Recognition

Masip¹,

Lapedriza²,

Vitrià³

2008

Proceedings of the Third International Conference on Computer Vision Theory and Applications

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Abstract:Usually face classification applications suffer from two important problems: the number of training samples from each class is reduced, and the final system usually must be extended to incorporate new people to recognize. In this paper we introduce a face recognition method that extends a previous boosting-based classifier adding new classes and avoiding the need of retraining the system each time a new person joins the system. The classifier is trained using the multitask learning principle and multiple verification tasks are trained together sharing the same feature space. The new classes are added taking advantage of the previous learned structure, being the addition of new classes not computationally demanding. Our experiments with two different data sets show that the performance does not decrease drastically even when the number of classes of the base problem is multiplied by a factor of 8.

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“…We propose to compare the GRBM's performance with the following wellknown linear mappings: Locality Preserving Projections (SLPP) [9], Locality Sensitive Discriminant Analysis (LSDA) [4] and Non-parametric Discriminant Analysis (NDA) [2].…”

Section: Grbm As a Non-linear Projection Techniquementioning

confidence: 99%

Restricted Boltzmann Machines for Gender Classification

Mansanet

Albiol

Paredes

et al. 2014

Lecture Notes in Computer Science

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Abstract. This paper deals with automatic feature learning using Gaussian Restricted Boltzmann Machines (GRBM) for the problem of gender recognition in face images. The GRBM is presented together with some practical learning tricks to improve the learning capabilities and speedup the training process. The performance of the features obtained is compared against several linear methods using the same dataset and the same evaluation protocol. The results show a classification accuracy improvement compared with classical linear projection methods. Moreover, in order to increase even more the classification accuracy, we have run some experiments where an SVM is fed with the non-linear mapping obtained by the GRBM in a tandem configuration.

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Nonparametric discriminant analysis and nearest neighbor classification

Cited by 100 publications

References 9 publications

Dimensionality reduction by minimizing nearest-neighbor classification error

Dimensionality reduction by minimizing nearest-neighbor classification error

MULTITASK LEARNING - An Application to Incremental Face Recognition

Restricted Boltzmann Machines for Gender Classification

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