Threshold Selection Study on Fisher Discriminant Analysis Used in Exon Prediction for Unbalanced Data Sets

This study aimed to construct a kernel Fisher discriminant analysis (KFDA) method from well logs for lithology identification purposes. KFDA, via the use of a kernel trick, greatly improves the multiclassification accuracy compared with Fisher discriminant analysis (FDA). The optimal kernel Fisher projection of KFDA can be expressed as a generalized characteristic equation. However, it is difficult to solve the characteristic equation; therefore, a regularized method is used for it. In the absence of a method to determine the value of the regularized parameter, it is often determined based on expert human experience or is specified by tests. In this paper, it is proposed to use an improved KFDA (IKFDA) to obtain the optimal regularized parameter by means of a numerical method. The approach exploits the optimal regularized parameter selection ability of KFDA to obtain improved classification results. The method is simple and not computationally complex. The IKFDA was applied to theIrisdata sets for training and testing purposes and subsequently to lithology data sets. The experimental results illustrated that it is possible to successfully separate data that is nonlinearly separable, thereby confirming that the method is effective.

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Revisiting the performance of PCA versus FDA versus Simple Projection for image recognition

Global Journal of Engineering and Technology Advances ◽

10.30574/gjeta.2021.8.1.0099 ◽

2021 ◽

Vol 8 (1) ◽

pp. 085-095

Author(s):

Fahad Bin Mostafa ◽

Md Sakhawat Hossain ◽

Md Easin Hasan

Keyword(s):

Discriminant Analysis ◽

Reduction Process ◽

Image Data ◽

Principal Component ◽

Machine Learning Techniques ◽

Data Sets ◽

Fisher Discriminant Analysis ◽

Linear Discriminant ◽

Fisher Discriminant ◽

Value Decomposition

In this paper, our main aim is to show a better dimension reduction process of high dimensional image data sets from several existing techniques. To verify it we start with most useful singular value decomposition to reduce the dimensionality of data to incorporate principal components. On the other hand, we classify data in advance to work out Fisher’s discriminant. From many real-world examples, we set a very well-known paradigm of analysis using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) or Fisher Discriminant Analysis (FDA) and Simple Projection (SP) to recognize people from their facial images. We consider that we have some images of known people that can be used to compare and recognize new images (of the same set of face images). Moreover, we show graphical and tabular representation for average performance of correct recognition as well as analyze the effectiveness of three different machine learning techniques.

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COMPREHENSIVE ANALYSIS FOR THE LOCAL FISHER DISCRIMINANT ANALYSIS

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001409007478 ◽

2009 ◽

Vol 23 (06) ◽

pp. 1129-1143 ◽

Cited By ~ 4

Author(s):

JUNBIN GAO ◽

PAUL W. H. KWAN ◽

XIAODI HUANG

Keyword(s):

Discriminant Analysis ◽

Data Sets ◽

Simulation Studies ◽

Local Data ◽

Fisher Discriminant Analysis ◽

Fisher Discriminant ◽

Practical Usefulness ◽

Higher Dimensional ◽

Local Fisher Discriminant Analysis ◽

New Formulation

Using local data information, the recently proposed local Fisher Discriminant Analysis (LFDA) algorithm18 provides a new way of handling the multimodal issues within classes where the conventional Fisher Discriminant Analysis (FDA) algorithm fails. Like the FDA algorithm (global counterpart), the LFDA suffers when it is applied to the higher dimensional data sets. In this paper, we propose a new formulation by which a robust algorithm can be formed. The new algorithm offers more robust results for higher dimensional data sets when compared with the LFDA in most cases. By extensive simulation studies, we have demonstrated the practical usefulness and robustness of our new algorithm in data visualization.

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Optimal multi-kernel local fisher discriminant analysis for feature dimensionality reduction and fault diagnosis

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x211009335 ◽

2021 ◽

pp. 1748006X2110093

Author(s):

Qing Zhang ◽

Heng Li ◽

Xiaolong Zhang ◽

Haifeng Wang

Keyword(s):

Fault Diagnosis ◽

Discriminant Analysis ◽

Feature Space ◽

High Dimensional ◽

Fisher Discriminant Analysis ◽

Vibration Signals ◽

Fisher Discriminant ◽

Local Fisher Discriminant Analysis ◽

Diagnosis Accuracy ◽

Diagnosis Model

To achieve a more desirable fault diagnosis accuracy by applying multi-domain features of vibration signals, it is significative and challenging to refine the most representative and intrinsic feature components from the original high dimensional feature space. A novel dimensionality reduction method for fault diagnosis is proposed based on local Fisher discriminant analysis (LFDA) which takes both label information and local geometric structure of the high dimensional features into consideration. Multi-kernel trick is introduced into the LFDA to improve its performance in dealing with the nonlinearity of mapping high dimensional feature space into a lower one. To obtain an optimal diagnosis accuracy by the reduced features of low dimensionality, binary particle swarm optimization (BPSO) algorithm is utilized to search for the most appropriate parameters of kernels and K-nearest neighbor (kNN) recognition model. Samples with labels are used to train the optimal multi-kernel LFDA and kNN (OMKLFDA-kNN) fault diagnosis model to obtain the optimal transformation matrix. Consequently, the trained fault diagnosis model implements the recognition of machinery health condition with the most representative feature space of vibration signals. A bearing fault diagnosis experiment is conducted to verify the effectiveness of proposed diagnostic approach. Performance comparison with some other methods are investigated, and the improvement for fault diagnosis of the proposed method are confirmed in different aspects.

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