نتایج جستجو برای: linear discriminant analysis lda

تعداد نتایج: 3168592  

2008
Sanaul Haq Philip J. B. Jackson James D. Edge

Recognition of expressed emotion from speech and facial gestures was investigated in experiments on an audio-visual emotional database. A total of 106 audio and 240 visual features were extracted and then features were selected with Plus l-Take Away r algorithm based on Bhattacharyya distance criterion. In the second step, linear transformation methods, principal component analysis (PCA) and li...

2009
Qiong Cheng Bo Fu Hui Chen

This paper proposes a new gait recognition method using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA is first applied to 1D time-varying distance signals derived from a sequence of silhouette images to reduce it’s dimensionality. Then, LDA is performed to optimize the pattern class ificovtion..And,Spatiotemporal Correlation (STC) and Normalized Euclidean Distan...

2013

In [2] feature graphs based on a wavelet transform, principle component analysis (PCA), and linear discriminant analysis (LDA) are compared. They reported 88%, 85% and 56% accuracy for PCA, LDA, and Gabor Wavelets respectively with a database containing 20 and individuals varying in gender, age, pose, and race. For each individual five images were used for testing, while one images was employed...

Journal: :J. Inf. Sci. Eng. 2010
Cheng-Yuan Zhang Qiu-Qi Ruan

An appearance-based face recognition approach called the L-Fisherfaces is proposed in this paper, By using Local Fisher Discriminant Embedding (LFDE), the face images are mapped into a face subspace for analysis. Different from Linear Discriminant Analysis (LDA), which effectively sees only the Euclidean structure of face space, LFDE finds an embedding that preserves local information, and obta...

2012
Luis Francisco Sánchez Merchante Yves Grandvalet Gérard Govaert

We present a novel approach to the formulation and the resolution of sparse Linear Discriminant Analysis (LDA). Our proposal, is based on penalized Optimal Scoring. It has an exact equivalence with penalized LDA, contrary to the multi-class approaches based on the regression of class indicator that have been proposed so far. Sparsity is obtained thanks to a group-Lasso penalty that selects the ...

2014
Wei Ge Lijuan Cai Chunling Han

Face recognition is a typical problem of pattern recognition and machine learning. Among these approaches to the problem of face recognition, subspace analysis gives the most promising results, and becomes one of the most popular methods. This paper researches typical subspace analysis approaches, based on the introduction of main approaches of linear subspace analysis, such as Principal Compon...

2003

Linear Discriminant Analysis (LDA) is one of the learning algorithms for the binary problems. One of the drawbacks of LDA is the degradation of its performance when it is difficult to approximate the boundary between two classes with a linear function, i.e. the boundary is non-linear. In this research, we apply Error-Correcting Output Codes (ECOC) to LDA. ECOC is well-known as one of the ensemb...

2014
W. Holmes Finch Jocelyn H. Bolin Ken Kelley

Classification using standard statistical methods such as linear discriminant analysis (LDA) or logistic regression (LR) presume knowledge of group membership prior to the development of an algorithm for prediction. However, in many real world applications members of the same nominal group, might in fact come from different subpopulations on the underlying construct. For example, individuals di...

2006
Hui Gao James W. Davis

We present a novel method for dimensionality reduction and recognition based on Linear Discriminant Analysis (LDA), which specifically deals with the Small Sample Size (SSS) problem in Computer Vision applications. Unlike the traditional methods, which impose specific assumptions to address the SSS problem, our approach introduces a variant of bootstrap bumping technique, which is a general fra...

2006
Ralf Schlüter András Zolnay Hermann Ney

In this paper, Linear Discriminant Analysis (LDA) is investigated with respect to the combination of different acoustic features for automatic speech recognition. It is shown that the combination of acoustic features using LDA does not consistently lead to improvements in word error rate. A detailed analysis of the recognition results on the Verbmobil (VM II) and on the English portion of the E...

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