Is discriminant analysis regression based?
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| Papers (9) | Insight |
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| In this paper, we propose a novel algorithm for discriminant analysis, called Spectral Regression Discriminant Analysis (SRDA). | |
20 Citations | We propose a novel linear discriminant analysis (LDA) approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies. |
| Normal discriminant analysis has been used as an alternative approach to this methodology, although it is strictly appropriate only when the usual normal discriminant assumptions concerning the explanatory variables are valid. | |
18 Citations | The results strengthen and support our conclusion and lead us to focus on the discriminant analysis by selecting only low numbers of discriminating variables. |
11 Aug 2013 122 Citations | In this paper, we will prove that the low-rank regression model is equivalent to doing linear regression in the linear discriminant analysis (LDA) subspace. |
14 Apr 2019 | Linear Discriminant Analysis is a classification technique which is easy to implement and has good accuracy. |
16 Citations | The analysis demonstrated the high capability of the linear discriminant analysis technique compared to other techniques. |
32 Citations | This study shows that specification of the form of the discriminant function may be one of the most important parts of a discriminant analysis. |
| We also show that the classical linear discriminant analysis (LDA) and its well-known extension - heteroscedastic linear discriminant analysis (HLDA) can be regarded as two special cases of our proposed method. |
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Importance of using regression analysis in research work?5 answersRegression analysis is crucial in research work due to its simplicity and power, making it a central quantitative tool in various fields. It aids in understanding mathematical relationships among variables, enabling the assessment of associations and predictions. Moreover, regression analysis helps in optimizing regression models, interpreting results, and identifying biases, enhancing the quality of research outcomes. Additionally, it plays a vital role in causal analysis, description, and inference in fields like International Relations and clinical research. By utilizing regression analysis, researchers can synthesize information effectively, measure mean and variance, and predict outcomes accurately, making it an indispensable tool for producing reliable and responsible research findings.
What are regression analysis rechniques?5 answersRegression analysis techniques encompass various methods used to analyze the relationship between dependent and independent variables. These techniques are crucial in statistical data analysis, providing insights into cause-effect relationships. Common regression models include linear regression, logistic regression, ridge regression, and polynomial regression. Linear regression fits linear data, while polynomial regression handles polynomial relationships in data sets. Logistic regression is ideal for binary predictions, and ridge regression addresses multicollinearity issues by penalizing correlated independent variables. Additionally, regularization methods like Lasso regression, which uses absolute values in the penalty function, help combat underfitting and overfitting problems in complex data sets. These techniques are essential for predictive modeling and data analysis across various fields.
What is discriminant analysis?5 answersDiscriminant analysis is a statistical technique used to analyze data when the dependent variable is categorical and the independent variables are parametric. It is used to determine the weightings of quantitative variables that best discriminate between different categories of the dependent variable. Discriminant analysis can be used to assess the accuracy of a classification system in predicting the group to which a sample belongs. It is commonly used in medical research and other fields where classification is required. Discriminant analysis is also viewed as a promising dimensionality reduction technique, particularly when there is a group structure in the data. It is used to classify data based on specific characteristics and can achieve a high level of classification success. Discriminant analysis has been applied in machine learning to analyze companies' performance based on financial data and assist in investment decision making. Linear discriminant analysis (LDA) is a widely studied variant of discriminant analysis that is used to extract discriminant features for characterizing different classes of objects.
What is Discriminant function analysis using leave-one-out classification?5 answersDiscriminant function analysis (DFA) is a data-reduction technique used to make decisions about naturally occurring group membership. It is a useful tool in applied research, particularly in psycholinguistic research. Leave-one-out classification is a method used to estimate the error rate of a classification procedure. It involves systematically leaving out one observation at a time from the training set and using the remaining observations to build a classification model. The error rate is then estimated by comparing the predicted class labels for the left-out observations with their true class labels. This method is commonly used in classification tasks, including multi-label classificationand class discriminant analysis in bioinformatics. The leave-one-out estimation can be combined with other techniques, such as bootstrap estimation, to improve the accuracy of the error rate estimation.
What is linear and quadratic discriminant analysis in machine learning?5 answersLinear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) are two fundamental classification methods in machine learning. LDA optimizes the decision boundary where the posteriors are equal, while QDA incorporates the ratio of determinants of dispersion matrices in addition to Mahalanobis distances. LDA and QDA are related to metric learning, kernel principal component analysis, Mahalanobis distance, logistic regression, Bayes optimal classifier, Gaussian naive Bayes, and likelihood ratio test. LDA and Fisher discriminant analysis are equivalent. GQDA is a nonparametric classification technique that generalizes discriminant analysis and is adaptive to given datasets. It compares favorably with other nonparametric methods and is computationally cost-effective. LDA is commonly used for dimensionality reduction as a preprocessing step in machine learning and pattern classification applications. It can be computed using class-dependent or class-independent methods. LDA can address Small Sample Size (SSS) and non-linearity problems, and solutions to these problems have been investigated. QDA is a simple and flexible classification tool with a quadratic number of parameters. DA is a classifier that calculates discriminant functions and decision boundaries based on covariance matrices of classes, and it can handle common or non-common covariance matrices.
What is discriptive analysis?5 answersDescriptive analysis refers to the use of statistical methods to summarize and describe data. It involves calculating measures such as ratios, percentages, standard deviation, skewness, and kurtosis to gain insights into the characteristics of a dataset. Descriptive analysis can be applied to various fields, including education, human capital formation, fishery sector, and medical research. For example, in the field of education, descriptive analysis has been used to examine the funding patterns and challenges in the education and health sectors in Nigeria. In the fishery sector, descriptive analysis has been used to analyze the export performance and challenges faced by fishermen in India. In medical research, descriptive analysis has been used to evaluate hematological abnormalities in lung, GIT, and breast malignancies. Overall, descriptive analysis provides a quantitative approach to understanding and summarizing data in various domains.