Questions tagged [discriminant-analysis]

Given multivariate data split into several subsamples (classes) the analysis finds linear combinations of variables, called discriminant functions, which discriminate between classes and are uncorrelated. The functions are applied then to assign old or new observations to the classes. Discriminant analysis is both dimensionality reduction and classification technique.

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Normalizing data from same variable but different individuals

I'm new to machine learning. I have the following scenario: I have five individuals that are each carrying an accelerometer. That sensor measures movement/acceleration on a scale from 0 to 255, 0 ...
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My data can be approximated with Normal mixture. How can I find the reasons and explain this behaviour?

I use DeLonge method to compare two ROC AUCS. The result of it is Z-score. Both ROC AUCs obtained from LDA (linear discriminant analysis) from sklearn package. The ...
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Difference between FDA and LDA

I have asked this question in Mathematics Stackexchange, thought however that it might be more fit for here: I am currently taking a Data-Analysis course and I learned about both the terms LDA (Linear ...
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Direct Discriminative Pattern Mining for Effective Classification - implementation

I'm looking for any actual working code implementation of the DDPMiner algorithm mentioned in the Direct Discriminative Pattern Mining for Effective Classification article form 2008 I'm having real ...
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Help with DDP Mining algorithm for Effective Classification of data sets from 2 groups

I'm trying to implement the DDPmine algorithm from this article as part of some project, and I do not understand where in the algorithm we use the Class Label of each transaction? We have transactions ...
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Linear discriminant analysis in R: how to choose the most suitable model?

The data set vaso in the robustbase library summarizes the vasoconstriction (or not) of subjects’ fingers along with their breathing volumes and rates. ...
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Iterative Reweighted Least Squares in python

I am trying to manually implement the irls logistic regression (Chapter 4.3.3 in Bishop - Pattern Recognition And Machine Learning) in python. For updating the weights, I am using $w' = w-(\Phi^TR\...
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Data discrimination after clustering

My task consists of two points: 1) Make data clustering; 2) Assign new data to the resulting clusters; I wanted to highlight the boundaries of clusters as min/max values ​​for each coordinate of an ...
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How to devise multicategory classifiers employing linear discriminant functions?

We might reduce the problem to $c$ two-class problems, where the $i^{th}$ problem is solved by a linear discriminant function that separates points assigned to $w_i$ from those not assigned to $w_1$. ...
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What does $\mathbf{w^Tx}+w_0$ graphically mean in the discriminant function?

I found a post explaining the discriminant function very detailed. But I am still confused about the function $g(\mathbf{x})=\mathbf{w^Tx}+w_0$ in 9.2 Linear Discriminant Functions and Decision ...
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Linear Discriminant - Least Squares Classification Bishop 4.1.3

Pls. refer section 4.1.3 in Pattern Recognition - Bishop: "Least squares for Classification": In a 2 class Linear Discriminat system, we classified vector $\mathbf{x}$ as $\mathcal{C}_1$ if ...
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Pattern Recognition, Bishop - (Linear) Discriminant Functions 4.1

Please refer "Pattern Recognition and Machine Learning" - Bishop, page 182. I am struggling to visualize the intuition behind equations 4.6 & 4.7. I am presenting my understanding of section 4.1....
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Prove GDA decision boundary is linear

My attempt: (a) I solved that $a=\ln{\frac{P(X|C_0)P(C_0)}{P(X|C_1)P(C_1)}}$ (b) Here is where I'm running into trouble. I'm plugging the distributions into $\ln{\frac{P(X|C_0)P(C_0)}{P(X|C_1)P(C_1)...
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Naive Bayes Classifier - Discriminant Function

To classify my samples, I decided to use Naive Bayes classifier, but I coded it, not used built-in library functions. If I use this equality, I obtain nice classification accuracy: ...
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Performace of Fischer projection as dimension reduction compared to other LDA methods

How is the performance of Fischer projection compared to other LDA methods of dimension reduction? I thought that Fischer projection was a great method of dimension reduction by maximizing class ...
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Convert a pdf into a conditional pdf such that mean increases and std dev falls

Let success metric(for some business use case I am working on) be a continuous random variable S. The mean of pdf defined on S indicates the chance of success. Higher the mean more is the chance of ...
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What is the favored discriminant analysis package in R?

I have been using the LDA package for R, but it is missing quite a few features especially those that can assess the output. Are the any preferred packages that have some of the following? ...
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Gaussian Discriminant Analysis (GDA) package in R

This stack exchange post - https://stats.stackexchange.com/questions/80507/what-is-a-gaussian-discriminant-analysis-gda - discusses GDA, a machine learning method for classification. I would like to ...
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Varying results when calculating scatter matrices for LDA

I'm following a Linear Discriminant Analysis tutorial from here for dimensionality reduction. After working through the tutorial (did the PCA part, too), I shortened the code using sklearn modules ...
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How are Hyperplane Heatmaps created and how should they be interpreted?

For nonlinear data, when we are using Support Vector Machines, we can use kernels such as Gaussian RBF, Polynomial, etc to achieve linearity in a different (potentially unknown to us) feature space ...
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Bayes Optimal Decision Boundaries for Gaussian Data with Equal Covariance

I am drawing samples from two classes in the two-dimensional Cartesian space, each of which has the same covariance matrix $[2, 0; 0, 2]$. One class has a mean of $[1.5, 1]$ and the other has a mean ...
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