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

0
votes
0answers
33 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
17 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
21 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 ...
4
votes
0answers
54 views

Compute and graph the LDA decision boundary

I saw an LDA plot with decision boundaries from The Elements of Statistical Learning: I understand that data are projected onto a lower-dimensional subspace. However, I would like to know how we get ...
0
votes
0answers
34 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
29 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
25 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
28 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
197 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
141 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
38 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
121 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 ...
5
votes
1answer
189 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
61 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
387 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
189 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
133 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
36 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
38 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
97 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
70 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
49 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
73 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
54 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
30 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
218 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
36 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
0answers
100 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
137 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 ...
1
vote
1answer
73 views

Optimization parameter for classification [closed]

I do not have enough knowledge about optimization. My problem is simple. Lets say I have 100 classes. Each class contains some instances (images). The feature vector obtained from a particular image ...
1
vote
0answers
82 views

Can saved regression factor scores be used to test for normality in spss and used for further analysis?

I have already collected my data for my dissertation and as I have many variables I have done a factor analysis to drill down the variables to few factors. Then I have computed the mean of all the ...
0
votes
0answers
348 views

PCA, LDA (Linear Discriminant Analysis), proportion of variance

I have some basic questions regarding PCA and LDA (Linear Discriminant Analysis ) but I am a bit lost and I will appreciate the help. In PCA there is a way to calculate the proportion of variance ...
1
vote
0answers
73 views

Interpreting weights from Fisher linear discriminant analysis

I have data that I'm trying to classify into two different groups using Fisher linear discriminant analysis. This gives me a vector of weights $\vec w$, used in the equation $\vec w\cdot \vec x$ to ...
5
votes
3answers
233 views

LDA vs. perceptron

I am trying to get a feel for how LDA 'fits' within other supervised learning techniques. I have already read some of the LDA-esque posts on here about LDA. I am already familiar with the perceptron, ...
3
votes
0answers
87 views

Linear Discriminant Analysis: Using subject as classification

I have a problem where I need to identify from which subject a particular set of data points came. More specifically, my problem is that I need to demonstrate that my subjects (N=9) can be ...
0
votes
1answer
363 views

Linear discriminant analysis in R

I have two matrices, both 46175 * 741 (Rows of variables by columns of individuals/observations). Matrix A contains a categorical (perhaps dependent) variable (0/1/2 or NA) and Matrix B is continuous ...
1
vote
0answers
236 views

Minimum training sample size required for a classifier

What is the best method to determine the minimum number of training samples required for a classifier? I am only comparing one classifier (four class problem), discriminant function analysis (DFA) ...
0
votes
0answers
25 views

What criteria are used to determine F to enter and F to remove in a discriminant analysis?

I'm studying the ontogeny of limb proportions in a fossorial rodent, and I want to describe differences among three age group vectors of different morphofunctional indices’ means with an stepwise ...
2
votes
2answers
469 views

Using principal components in a linear discriminant analysis for a diagnostic test

I am interested in building a linear discriminant function to discriminate between 2 groups, out of 60 variables. (I'm planning to select the most discriminative of the variables for a future ...
3
votes
0answers
141 views

Prediction using SVD and Fisher's linear discriminant

Where can I get an explanation of the procedure used when making a prediction using SVD? Let me elaborate a bit more. Suppose you have data in a matrix $A$ containing two classes. In particular, you ...
1
vote
1answer
132 views

Why is there a sharp elbow in my ROC curves?

I have some EEG data sets that I am testing against two classes. I can get a decent error rate from LDA (the class-conditional distributions aren't Gaussian, but have similar tails and good enough ...
0
votes
0answers
86 views

How does R{MASS} lda function use MLEs to improve its result?

I am using the LDA function in the MASS package of R, which has the following specification: ...
2
votes
1answer
384 views

What exactly the ROC curve can tell us or can be inferred?

(I post this originally at http://stackoverflow.com/questions/15477282/what-exactly-the-roc-curve-can-tell-us-or-can-be-inferred, but people directed me to here. Sorry about posting this twice.) I ...
1
vote
0answers
161 views

Using linear discriminant analysis to validate the cluster groups resulting from kmeans

I'm currently working on a cluster analysis project and ran kmeans on the data for k=2. I was reading similar articles on similar experiments, and the investigators used discriminant analysis to ...
1
vote
1answer
62 views

Inconsistency in cross-validation results

I have a set of dataset recorded from subjects as they perform some particular cognitive task. The data consists of 16 channels and a number of sample points per channel and I want to classify this ...
3
votes
3answers
791 views

Does PCA followed by LDA make sense?

This is a question about classification. I am a neuroscience student with little experience of classification methods and I'd be grateful for any advice about the best way to implement a linear ...
5
votes
2answers
553 views

Fisher discrimination power of a variable and Discriminant analysis. Algebra of LDA

Apparently, the Fisher analysis aims at simultaneously maximising the between-class separation, while minimising the within-class dispersion. A useful measure of the discrimination power of a ...
2
votes
2answers
381 views

Where does the definition of the hyperplane in a simple SVM come from?

I'm trying to figure out support vector machines using this resource. On page 2 it is stated that for linearly separable data the SVM problem is to select a hyperplane such that $\vec{x}_i\vec{w} + b ...
1
vote
0answers
88 views

How to interpret this output in Dimension Reduction?

I'm running Linear Discriminant Analysis on a dataset and then performing clustering on it. I'm reducing it to dimensions 2,6,10. On comparing metrics like Accuracy and Normalized Mutual Information, ...
0
votes
1answer
78 views

Is “discriminant function” a synonym for “classification function”

"discriminant function" and "classification function" are two terms used in literature to denote a a function that maps a feature vector into a discrete class variable. I presume "discriminant ...