Topic
Linear discriminant analysis
About: Linear discriminant analysis is a research topic. Over the lifetime, 18361 publications have been published within this topic receiving 603195 citations. The topic is also known as: Linear discriminant analysis & LDA.
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TL;DR: The new discriminant analysis with orthonormal coordinate axes of the feature space is proposed, which is more powerful than the traditional one in so far as the discriminatory power and the mean error probability for coordinate axes are concerned.
153 citations
TL;DR: The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion and displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection.
Abstract: We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.
152 citations
TL;DR: This paper proposes a novel semi-supervised orthogonal discriminant analysis via label propagation that propagates the label information from the labeled data to the unlabeled data through a specially designed label propagation, and thus the distribution of the unl labeled data can be explored more effectively to learn a better subspace.
152 citations
TL;DR: Several algorithms for preprocessing, feature extraction, pre-classification, and main classification, and modified Bayes classifier and subspace method for the robust main classification are experimentally compared to improve the recognition accuracy of handwritten Japanese character recognition.
152 citations
25 Aug 2004
TL;DR: Results are promising, indicating that LDA and the proposed ensemble framework have sufficient discriminative power for the ethnicity classification problem and the normalized ethnicity classification scores can be helpful in the facial identity recognition.
Abstract: Human facial images provide the demographic information, such as ethnicity and gender. Conversely, ethnicity and gender also play an important role in face-related applications. Image-based ethnicity identification problem is addressed in a machine learning framework. The Linear Discriminant Analysis (LDA) based scheme is presented for the two-class (Asian vs. non-Asian) ethnicity classification task. Multiscale analysis is applied to the input facial images. An ensemble framework, which integrates the LDA analysis for the input face images at different scales, is proposed to further improve the classification performance. The product rule is used as the combination strategy in the ensemble. Experimental results based on a face database containing 263 subjects (2,630 face images, with equal balance between the two classes) are promising, indicating that LDA and the proposed ensemble framework have sufficient discriminative power for the ethnicity classification problem. The normalized ethnicity classification scores can be helpful in the facial identity recognition. Useful as a "soft" biometric, face matching scores can be updated based on the output of ethnicity classification module. In other words, ethnicity classifier does not have to be perfect to be useful in practice.© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
152 citations