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 ...

learn more… | top users | synonyms

1
vote
1answer
37 views

Does Fisher linear discriminant analysis (LDA) require normal distribution of the data in each class?

Does Fisher linear discriminant analysis really require the data distribution in each category to be normal? I see two versions. The first one states that it requires the normal distribution and ...
0
votes
1answer
39 views

Bayes' factor vs. Bayes' Discriminant Rule

When we are comparing two models against some data, will we obtain the same (set of) posterior odds for the models both when we use the Bayes' factor and when we use the discriminant rule? If not, ...
2
votes
2answers
91 views

Reproduce linear discriminant analysis projection plot

I'm struggling with projection points in linear discriminant analysis (LDA). Many books on multivariate statistical methods illustrate the idea of the LDA with the figure below. The problem ...
2
votes
2answers
86 views

Post-hoc tests for MANOVA

I am using a MANOVA test to compare nine different dependent variables (from neuropsychological and neuropsychiatric assessment) between three groups. The output shows a significant influence from ...
1
vote
1answer
22 views

Does it make sense to calculate Q2 and R2 values on PLS-DA models?

Since PLS-DA is a computational technique which deals with outcomes expressed as a categorical variable (e.g. "Yellow","Brown","Black","Green") I cannot understand how it is possible to calculate Q2 ...
2
votes
1answer
38 views

Linear Discriminant Analysis and non-normal distributed data

If I understand correctly, a Linear Discriminant Analysis (LDA) assumes normal distributed data, independent features, and identical covariances for every class for the optimality criterion. Since ...
7
votes
3answers
271 views

Why Python's scikit-learn LDA results are different from LDA in R or a step-by-step approach

I was using the Linear Discriminant Analysis (LDA) from the scikit-learn machine learning library (Python) for dimensionality reduction and was a little bit curious about the results. I am wondering ...
2
votes
1answer
55 views

Standardizing before/after/at all when using multi-class LDA for pre-processing step

If a multi-class Linear Discriminant Analysis (or I also read Multiple Discriminant Analysis sometimes) is used for dimensionality reduction (or transformation after dimensionality reduction via PCA), ...
3
votes
3answers
94 views

Is linear discriminant analysis (LDA) more likely to overfit than support vector machine (SVM)?

I went to a short talk and the speaker quickly mentioned something like 'LDA (linear discriminant analysis) is more likely to be overfitted than SVM (support vector machine)'. Is this true? And why?
3
votes
1answer
59 views

Multi-class classification via all pairwise classifications with LDA

I have trained linear discriminant analysis (LDA) classifiers for three classes of the IRIS data and struggling with how to make the classification. Here is the procedure: For the Iris data, I have 3 ...
0
votes
0answers
24 views

How to check discriminant analysis assumption in R using lda?

I want to use lda in MASS package in R. According to the theory behind that, first need to validate the assumption. Actually I've found some example from the net but they did not bother to validate ...
3
votes
2answers
141 views

Best practice for dimensionality reduction using Principal Component Analysis (PCA) and/or Linear Discriminant Analysis (LDA)

Assume I have a dataset for a supervised statistical classification task, e.g., via a Bayes' classifier. This dataset consists of 20 features and I want to boil it down to 2 features via ...
0
votes
0answers
33 views

Multiple Discriminant Analysis, Linear Discriminant Analysis, and Multidimensional scaling - how are they related?

Some time ago when I took a Pattern Classification class, the "concept" was introduced as Multiple Discriminant Analysis: You want to project your data onto a subspace (if you are interested in ...
2
votes
1answer
52 views

A question on discriminant analysis- Linear discriminant function

Above is part of an examination paper. I am not sure how to understand this SAS output. Especially what is there in the last table which looks to me like two discriminant functions. Can someone ...
0
votes
0answers
47 views

Discriminant analysis

I want to do a discriminant analysis for my study. This study consist of one dependent variable and 8 independent variables. The dependent variable is categorical and has 2 groups. The independent ...
0
votes
0answers
78 views

LDA - Why differents formulas to calculate covariance and pooled covariance matrix

Reading materials from differents sites some questions have risen about covariance and the pooled covariance matrix calculation to implement LDA: Definitions Ci - covariance matrix of group i (C1 and ...
1
vote
0answers
52 views

Why do we not look at the covariance matrix when choosing between LDA or QDA

I understand the difference between LDA and QDA (linear and quadratic discriminant analysis), being that with LDA assume that your features have the same covariance matrix in each class. I wonder why ...
0
votes
1answer
39 views

Modeling: Option to cross-validate and predict afterwards

This is beyond a R question, But why doesn't it make sense to fit a model, say, a linear discriminant (LDA) model, with leave-out-one cross validation, and afterwards to use this model to predict a ...
1
vote
0answers
11 views

Statistical technique to assess nutrition

My goal is to find risk factors for a disease. I think that a malnutrition is a risk factor for this disease. I have 5 variables that indicate the frequencies of ...
3
votes
2answers
146 views

Logistic regression vs. LDA as two-class classifiers

I am trying to wrap my head around the statistical difference between Linear discriminant analysis and Logistic regression. Is my understanding right that, for a two class classification problem, LDA ...
0
votes
0answers
18 views

How to discriminate the two different classes using the info from histograms

I have the data from 12 patients and I know the responders and the non responders. I already have calculated some markers from each one and plotting histograms and pdfs it seems that they can be ...
0
votes
0answers
64 views

Coefficients of Linear Discriminants in R

I've read the answers in What are "coefficients of linear discriminants" in LDA?, but I still don't understand what coefficients of linear discriminants on output of R means. What is it? ...
1
vote
1answer
29 views

Similarity Coefficient and Geochemical Correlation

I have a set of around 160 samples from different sediment bodies, or stratigraphic units, which I am attempting to correlate using geochemical analysis of 25 elements for each sample, and a ...
0
votes
0answers
52 views

Linear Discriminant Analysis matrix dimension

I am trying to implement Linear Discriminant Analysis for face recognition. I have 3 classes and each classes have 10 image each. The dimension of matrix in class A, B and C is 10*500 .So each row ...
10
votes
1answer
324 views

Compute and graph the LDA decision boundary

I saw an LDA (linear discriminant analysis) plot with decision boundaries from The Elements of Statistical Learning: I understand that data are projected onto a lower-dimensional subspace. However, ...
0
votes
0answers
196 views

How to interpret the LDA output in R?

Basically, I have following LDA output (below). My question is how do I actually interpret it ? I could not find much resources on the web. Especially, what does "Group Means" really mean here ? I was ...
0
votes
0answers
45 views

Within scatter matrix linear discriminant analysis

I am trying to implement Linear Discriminant Analysis. I have 10 classes and each class has 3 observations at various instances: ...
0
votes
0answers
60 views

Standardized LDA loadings

I'm having issue finding code for calculating standardized loadings for LDA, I wrote a code for it, but I'm not sure is this the correct way to standardize the loadings. ...
1
vote
0answers
45 views

Estimating the covariance matrix in LDA

I was trying to derive the equations from page 109 in "elements of statistical learning" (image below) To be honest, I am not sure how the covariance $\Sigma$ is estimated (the third bullet point in ...
4
votes
1answer
269 views

Bayesian and Fisher's approaches to linear discriminant analysis

I know 2 approaches to do LDA, the Bayesian approach and the Fisher's approach. Suppose we have the data $(x,y)$, where $x$ is the $p$-dimensional predictor and $y$ is the dependent variable of $K$ ...
1
vote
0answers
500 views

What are “coefficients of linear discriminants” in LDA?

In R, I use lda function from library MASS to do classification. As I understand LDA, input ...
0
votes
0answers
88 views

How can a standardized canonical coefficient be greater than 1.0 in a discriminant function analysis?

I have a data that I have used discriminant function analysis with. In the results, one variable has a standardized canonical function coefficient that is greater than 1.0. I didn't think these could ...
2
votes
1answer
213 views

How to interpret this cross-validated sparse LDA figure using CARET package?

Training data with $p$ =11 predictors and $n$ =165 with 4-class problem was cross-validated (5 times repeated 10-fold CV) using the sparse LDA (aka SDA) using caret ...
6
votes
1answer
303 views

How is MANOVA related to LDA?

In several places did I see a claim that MANOVA is like ANOVA plus linear discriminant analysis (LDA), but it was always made in a hand-waving sort of way. I would like to know what exactly it is ...
1
vote
0answers
86 views

Clarification on LDA and the multivariate Gaussian

From my understanding, to calculate the posterior probability of a sample $x$ belonging to a class $k$ using Linear Discriminant Analysis you would first calculate the eigenvector matrix $W$ required ...
5
votes
1answer
1k views

Can the scaling values in a linear discriminant analysis (LDA) be used to plot explanatory variables on the linear discriminants?

Using a biplot of values obtained through principal component analysis, it is possible to explore the explanatory variables that make up each principle component. Is this also possible with Linear ...
1
vote
2answers
327 views

What is a Gaussian Discriminant Analysis (GDA)?

What is a Gaussian Discriminant Analysis (GDA)? What materials should one read to understand how a GDA works and where it comes from? Try to explain this for someone at a high-school level.
2
votes
1answer
139 views

Finding the kink in a bivariate relationship

I'm investigating which methods are generally used to dichotomise an ordinal variable Y so that it maximises the between-group differences in the values of X and minimises the within-group differences ...
1
vote
0answers
42 views

What's the methodology behind the most-difference-between-groups-tag-cloud?

What is the likely stats methodology used in this old OKCupid post?: http://www.economist.com/blogs/johnson/2010/10/sexuality_and_language And this: ...
1
vote
0answers
39 views

Role of orthogonal constraints (X^TX=I) in linear discriminant analysis?

What is the role of the orthogonal constraints in Linear Discriminant Analysis? Why would it work/not work if the fraction of the traces is maximized without that constraint? Wouldn't its still ...
1
vote
0answers
127 views

Quadratic discriminant analysis (QDA) with qualitative predictors in R

I need your help with a Statistical Learning homework in R. I have to perform classification over this dataset: mammographic masses predicting Severity (0="not severe",1 = "severe) using these ...
0
votes
1answer
118 views

Posterior probability

Suppose that we have have scoring functions $f(\textbf{x})$ and $g(\textbf{x})$ for classifying an object as red or blue. These are based on linear discriminant analysis. So if $f(\textbf{x}) > ...
0
votes
0answers
61 views

Linear Discrimination Scores

Suppose we have 4 groups of animals (pig, dog, rabbit, cat). There are 10 animals in each group. We measure the weight and pulse of each animal. Consider the following procedure in SAS: ...
1
vote
1answer
98 views

Discriminant analysis for the validation of cluster analysis

I did a discriminant analysis for the validation of my cluster analysis. The cluster analysis is based on a PCA, so I used the components as the independant variables in the discriminant analysis. My ...
0
votes
1answer
59 views

Linear Discriminant Function

In linear discriminant analysis, how is the linear discriminant function determined? Assuming equal variance-covariance matrices, is the linear discriminant function determined from the training data? ...
0
votes
0answers
37 views

Is it discriminant analysis?

I have samplings of one-dimensional data of two classes: $A$ and $B$. I have to predict the posterior probability of class $A$. $$\tilde{P}(A|x) \approx \frac{N_A\tilde{f_A}(x)}{N_A\tilde{f_A}(x) + ...
6
votes
1answer
535 views

Three versions of discriminant analysis: differences and how to use them

Can anybody explain differences and give specific examples how to use these three analyses? LDA - Linear Discriminant Analysis FDA - Fisher's Discriminant Analysis QDA - Quadratic Discriminant ...
1
vote
0answers
78 views

Difference between probabilistic generative, discriminative models and “discriminant functions”?

I have been thoroughly confused by a bunch of online resources regarding this issue. I would really like a simple explanation about the differences between generative and discriminative approaches, ...
0
votes
1answer
144 views

Linear Discriminant (decision region) singly connected and convex?

I am reading about linear discriminants, and have encountered a phrase that I have no idea about. The phrase says that a decision region constructed in a certain way, is "singly connected and convex", ...
4
votes
1answer
223 views

Alternatives to stepwise discriminant analysis for feature selection on hyperspectral data

I am new to R and to hyperspectral data analysis. However, in my research, I have found that many warn against using Stepwise discriminant analysis (using Wilk's Lambda or Mahalanobis distance) for ...