Fisher Discriminative Analysis Research Articles

Group-sparsity-enforcing fault discrimination and estimation with dynamic process data

Fisher discriminative analysis (FDA) has been recognized a prototypical approach to fault classification and diagnosis. To enhance model performance with time-series data used, it is customary to encompass lag measurements into the model. This not only increases model complexity prohibitively but also reduces the interpretability of fault diagnosis strategies. To address this issue, we propose a novel group-sparsity-enforcing FDA model, which utilizes reweighted group Lasso penalty to prune out irrelevant variables at the variable level so as to improve interpretability of discriminant directions. A tailored algorithm based on alternating direction method of multipliers is developed to solve the non-smooth and non-convex optimization problem. In addition, to identify root cause variables and unveil the fault evolution over time, a sparse fault estimation approach based on reweighted group Lasso is developed. This eventually allows to develop a holistic online scheme yielding informative diagnostic verdicts with faulty variable information used. Experimental results demonstrate that, the proposed model significantly improves the discriminant capability between normal and faulty data, and yields more interpretable discriminative information than conventional methods using dynamic process data.

Adaptive Discriminative Deep Correlation Filter for Visual Object Tracking

Correlation filter trackers building on deep convolution neural networks (CNNs) contribute efficient visual object trackers but remain challenged with severe target appearance variations. The reason for this is that CNNs trained for image classification tasks are less discriminative to the dynamic variations of targets and backgrounds. In this paper, we propose an adaptive discriminative deep correlation filter (adaDDCF), which, by incorporating discriminative feature fine-tuning with adaptive appearance modeling, pursues stable object tracking in complex backgrounds. In adaDDCF, a convolutional Fisher discriminative analysis (FDA) layer is implemented for positive and negative instance mining and scene-specific feature learning. A correlation layer is then embedded to learn the correlation response of consecutive frames for target appearance modeling. With an online learning procedure using forward–backward propagation, the FDA layer and the correlation layer are effectively coupled, leading to effective and discriminative fine-tuning for the proposed tracker, which consequently alleviates the target drifting problem. Extensive experiments on the challenging benchmarks OTB2013, OTB2015, and OTB50 demonstrate that the proposed adaDDCF tracker outperforms many state-of-the-art trackers.

Weighted estimation for texture analysis based on Fisher discriminative analysis

The traditional texture analysis methods only use relative contribution of each face area to mark the global similarity. For solving the problem of feature extraction which cause by local instead of global, weighted estimation for texture analysis (WETA) method based on the Fisher discriminative analysis (FDA) is proposed. First, local binary pattern (local binary pattern, LBP) or partial phase quantisation (local phase quantisation, LPQ) is used for image texture encoding. Then, the image is divided into local small pieces which are all equal and not overlap. The most discrimination axis, which are extracted from similarity space, are applied into texture analysis by FDA method, then the best solution through weight optimisation is given. Finally, experiments on two major general face databases (FERET and FEI) verify the effectiveness of the proposed method. The proposed method receives a recognition rate of 96% in LPQ and FERET combination. The experimental results show that compared with texture methods in other papers, the proposed method in this paper has obtained better recognition performance.

Weighted estimation for texture analysis based on Fisher discriminative analysis

The traditional texture analysis methods only use relative contribution of each face area to mark the global similarity. For solving the problem of feature extraction which cause by local instead of global, weighted estimation for texture analysis (WETA) method based on the Fisher discriminative analysis (FDA) is proposed. First, local binary pattern (local binary pattern, LBP) or partial phase quantisation (local phase quantisation, LPQ) is used for image texture encoding. Then, the image is divided into local small pieces which are all equal and not overlap. The most discrimination axis, which are extracted from similarity space, are applied into texture analysis by FDA method, then the best solution through weight optimisation is given. Finally, experiments on two major general face databases (FERET and FEI) verify the effectiveness of the proposed method. The proposed method receives a recognition rate of 96% in LPQ and FERET combination. The experimental results show that compared with texture methods in other papers, the proposed method in this paper has obtained better recognition performance.

Manifold Transfer Subspace Learning (MTSL) for Applications in Aided Target Recognition

This article describes how transfer subspace learning has recently gained popularity for its ability to perform cross-dataset and cross-domain object recognition. The ability to leverage existing data without the need for additional data collections is attractive for monitoring and surveillance technology, specifically for aided target recognition applications. Transfer subspace learning enables the incorporation of sparse and dynamically collected data into existing systems that utilize large databases. Manifold learning has also gained popularity for its success at dimensionality reduction. In this contribution, Manifold learning and transfer subspace learning are combined to create a new system capable of achieving high target recognition rates. The manifold learning technique used in this contribution is diffusion maps, a nonlinear dimensionality reduction technique based on a heat diffusion analogy. The transfer subspace learning technique used is Transfer Fisher's Linear Discriminative Analysis. The new system, manifold transfer subspace learning, sequentially integrates manifold learning and transfer subspace learning. In this article, the ability of the new techniques to achieve high target recognition rates for cross-dataset and cross-domain applications is illustrated using a variety of diverse datasets.

Medical image classification based on multi-scale non-negative sparse coding.

With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance.

A New Quantitative Metric for Liver Classification from Ultrasound Images

This paper presents a new quantitative metric which can be used as indirect method for characterization of liver from ultrasound images. This new metric is inspired from the visual criterion considered by the radiologists through texture and echogenicity of the Liver in ultrasound image. The proposed metric is a single parameter extracted from 6 texture features on the basis of Homogeneity, Roughness, Contrast, Granularity and Orientation of the liver surface for classification into Fatty or Normal liver. The main contribution of the present work is that the best texture features are selected on the basis of Fisher's linear discriminative analysis and then formulated into a single metric by considering their weightage similar to the visual criterion used for liver classification.

Human Action Recognition Based on Discriminative Sparse Coding Video Representation

The bag-of-words(BoW) model usually causes large errors and weak discrimination in video representation in video action recognition,and affects the human action recognition accuracy.To solve this problem,a discriminative sparse coding(DSC) video representation algorithm is proposed.It’s a sparse coding framework involving a Fisher discriminative analysis to encode video local spatial-temporal features and increase the video sparse representation discrimination.And an online discriminative dictionary learning algorithm is also proposed to train a dictionary from massive video data.The experiments show that,comparing with the existing algorithms,the proposed algorithm effectively improves human action recognition accuracy.

Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder

In this study, a resting-state fMRI based classifier, for the first time, was proposed and applied to discriminate children with attention-deficit/hyperactivity disorder (ADHD) from normal controls. On the basis of regional homogeneity (ReHo), a mapping of brain function at resting state, PCA-based Fisher discriminative analysis (PC-FDA) was trained to build a linear classifier. Permutation test was then conducted to identify the brain areas with the most significant contribution to the final discrimination. Experimental results showed a correct classification rate of 85% using a leave-one-out cross-validation. Moreover, some highly discriminative brain regions, like the prefrontal cortex and anterior cingulate cortex, well confirmed the previous findings on ADHD. Interestingly, some important but less reported regions such as the thalamus were also identified. We conclude that the classifier, using resting-state brain function as classification feature, has potential ability to improve current diagnosis and treatment evaluation of ADHD.