when the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. in this paper, we propose a supervised feature extraction method based on discriminant analysis (da) which uses the first principal component (pc1) to weight the scatter matrices. the proposed method, called da-pc1, copes with the small sample size problem and has...

We describe use of Linear Discriminant Analysis (LDA) for data-driven automatic design of RASTA-like lters. The LDA applied to rather long segments of time trajectories of critical-band energies yields FIR lters to be applied to these time trajectories in the feature extraction module. Frequency responses of the rst three discriminant vectors are in principle consistent with the ad hoc designed...

Linear Discriminant Analysis (LDA) is a popular feature extraction technique for face image recognition and retrieval. However, It often suffers from the small sample size problem when dealing with the high dimensional face data. Two-step LDA (PCA+LDA) [1–3] is a class of conventional approaches to address this problem. But in many cases, these LDA classifiers are overfitted to the training set...

In this paper we explore the effect of long-span features, resulting from concatenating multiple speech frames and projecting the resulting vector onto a subspace using Linear Discriminant Analysis (LDA) techniques. We show that LDA is not always effective in selecting the optimal combination of long-span features, and introduce a discriminative feature analysis method that seeks to minimize ph...

Linear Discriminant Analysis (LDA) is a popular technique for supervised dimensionality reduction, and its performance is satisfying when dealing with Gaussian distributed data. However, the neglect of local data structure makes LDA inapplicable to many real-world situations. So some works focus on the discriminant analysis between neighbor points, which can be easily affected by the noise in t...

In India and across the globe, liver disease is a serious area of concern in medicine. Therefore, it becomes essential to use classification algorithms for assessing the disease in order to improve the efficiency of medical diagnosis which eventually leads to appropriate and timely treatment. The study accordingly implemented various classification algorithms including linear discriminant analy...

An “electronic nose” has been used for the detection of adulterations of sesame oil. The system, comprising 10 metal oxide semiconductor ensors, was used to generate a pattern of the volatile compounds present in the samples. Prior to different supervised pattern recognition treatments, eature extraction techniques were employed to choose a set of optimal discriminant variables. Principal compo...

Linear discriminant analysis (LDA) is one of the most popular dimension reduction methods, but it is originally focused on a single-labeled problem. In this paper, we derive the formulation for applying LDA for a multi-labeled problem. We also propose a generalized LDA algorithm which is effective in a high dimensional multi-labeled problem. Experimental results demonstrate that by considering ...

Multi-label problems arise frequently in image and video annotations, and many other related applications such as multi-topic text categorization, music classification, etc. Like other computer vision tasks, multi-label image and video annotations also suffer from the difficulty of high dimensionality because images often have a large number of features. Linear discriminant analysis (LDA) is a ...

Fisher linear discriminant analysis (LDA) and its kernel extension— kernel discriminant analysis (KDA)—are well known methods that consider dimensionality reduction and classification jointly. While widely deployed in practical problems, there are still unresolved issues surrounding their efficient implementation and their relationship with least mean squared error procedures. In this paper we ...