# linear discriminant analysis python

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### Fisher LinearDiscriminantAnalysis (Fisher LinearDiscriminantAnalysis)

From: http://blog.csdn.net/warmyellow/article/details/5454943 I. lda algorithm Overview: Linear Discriminant Analysis (LDA), also known as Fisher linear discriminant, is a classic algorithm for pattern recognition, it introduced Pattern Recognition and AI by belhumeur in 199

### Python implementations of machine learning Algorithms (1): Logistics regression and lineardiscriminantanalysis (LDA)

') plt.ylabel (' Ratio_sugar ') plt.title (' LDA ') plt.show () W=calulate_w () plot (W)The results are as follows: The corresponding W value is:[ -6.62487509e-04, -9.36728168e-01]Because of the relationship between data distribution, LDA's effect is not obvious. So I changed the number of samples of several label=0, rerun the program to get the result as follows:The result is obvious, the corresponding W value is:[-0.60311161,-0.67601433]Transferred from: http://cache.baiducontent.com/c?m= 9d7

### Analysis of lineardiscriminantanalysis (Lineardiscriminant analytical, LDA) algorithm

Introduction to LDA algorithmA LDA Algorithm Overview:Linear discriminant Analysis (Linear discriminant, LDA), also called Fisher Linear discriminant (Fisher Linear

### LinearDiscriminantAnalysis-LDA-LinearDiscriminantAnalysis

1. What is lda? Linear Discriminant Analysis (LDA. Fisher Linear Discriminant (linear) is a classic algorithm for pattern recognition. In 1996, belhumeur introduced Pattern Recognition and AI. The basic idea is to project a high-d

### LineardiscriminantAnalysis (Lineardiscriminant analytical, LDA) algorithm initial knowledge

Introduction to LDA algorithmA. LDA Algorithm Overview:Linear discriminant Analysis (Linear discriminant, LDA), also called Fisher Linear discriminant (Fisher Linear

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### "Reprint" Lineardiscriminantanalysis (Lineardiscriminant analyses) (i)

Linear discriminant Analysis (Linear discriminant Analyst) (i)1. QuestionsBefore we discussed the PCA, ICA or the sample data to say, can be no category tag Y. Recall that when we do the regression, if there are too many features, then there will be irrelevant features intro

### "Reprint" Lineardiscriminantanalysis (Lineardiscriminant analyses) (ii)

Linear discriminant Analysis (Linear discriminant Analyst) (ii)4. ExampleThe spherical sample points on the 3-dimensional space are projected onto two dimensions, and W1 can achieve better separation than W2.Comparison of the dimensionality reduction between PCA and LDA:The

### A concise introductory course for lineardiscriminantanalysis

classification degree. Ronald Fisher presented a linear discriminant (Problems Linear) method in 1936 (the "Use of multiple measurements in taxonomic discriminant"), which is sometimes used to solve classification questions Problem. The initial linear

### Mathematical-lineardiscriminantanalysis (LDA) in machine learning, principal component Analysis (PCA) "4"

of the solution of a matrix eigenvalue problem, but understand how to deduce, in order to understand the meaning of the deeper. This content requires the reader to have some basic linear algebra basis, such as eigenvalue, eigenvector concept, space projection, point multiplication and other basic knowledge. In addition to the other formulas, I try to speak more simple and clear.Lda:The full name of LDA is linear

### Mathematics in Machine learning (4)-Lineardiscriminantanalysis (LDA), principal component analysis (PCA)

of the solution of a matrix eigenvalue problem, but understand how to deduce, in order to understand the meaning of the deeper. This content requires the reader to have some basic linear algebra basis, such as eigenvalue, eigenvector concept, space projection, point multiplication and other basic knowledge. In addition to the other formulas, I try to speak more simple and clear.Lda:The full name of LDA is linear

### Mathematics in Machine learning (4)-Lineardiscriminantanalysis (LDA), principal component analysis (PCA)

of the solution of a matrix eigenvalue problem, but understand how to deduce, in order to understand the meaning of the deeper. This content requires the reader to have some basic linear algebra basis, such as eigenvalue, eigenvector concept, space projection, point multiplication and other basic knowledge. In addition to the other formulas, I try to speak more simple and clear.Lda:The full name of LDA is linear

### Mathematics in Machine Learning (4)-lineardiscriminantanalysis (LDA) and principal component analysis (PCA)

feature values. However, only by understanding how to derive them can we have a deeper understanding of the meaning. This article requires readers to have some basic linear algebra basics, such as the concept of feature values, feature vectors, spatial projection, and dot multiplication. I will try to make it easier and clearer about other formulas. LDA: The full name of LDA is linear

### LinearDiscriminantAnalysis (LDA)

I. Basic Thoughts of LDA Linear Discriminant Analysis (LDA), also known as Fisher linear discriminant, is a classic algorithm for pattern recognition, it introduced Pattern Recognition and AI by belhumeur in 1996. The basic idea of linea

### Machine Learning-feature selection (Dimension Reduction) LinearDiscriminantAnalysis (LDA)

Feature Selection (Dimension Reduction) is an important step in data preprocessing. For classification, feature selection can select the features most important to classification from a large number of features to remove noise from the original data. Principal Component Analysis (PCA) and linear discriminant analysis (

### Feature selection (dimensionality reduction) lineardiscriminantanalysis (LDA)

Previously, LDA was used to classify, and PCA was used for dimensionality reduction. The dimensionality reduction of PCA is to reduce the amount of subsequent computations, and the ability to distinguish different classes is not improved. PCA is unsupervised, and LDA is able to project different classes in the best direction, so that the distance between the two categories is the largest, to achieve easy-to-distinguish purposes, LDA is supervised. The following blog post is a good account of the

### LinearDiscriminantAnalysis (1)

obtain some of the best features (most closely related to Y) after dimensionality reduction ), what should we do? 2. Linear Discriminant Analysis (Case 2) Review our previous logistic regression method. Given m n-dimensional feature training samples (I from 1 to m), each corresponds to a class label. We just need to learn the parameters so that (G is the sig

### Lineardiscriminantanalysis in R language

in the R language, linear discriminant analysis (Liner discriminant analyses, or LDA) is implemented by using thelinear discriminant function Lqa () in the package mass. The function has three invocation formats:1) When the object is data.frame the data frameLDA (X,grouping,

### Machine learning dimensionality reduction algorithm two: LDA (Lineardiscriminantanalysis)

The amount of distance on a blog has been a long time, has been busy to do a job, recently finished, or to write blog ah. A lot of basic knowledge some forgotten, also counted as a kind of review. I try to derive the key place to write, suggesting that you still want to manually push a formula to increase understanding. Linear discriminant Analysis (also known a

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