Feature extraction using fuzzy complete linear discriminant analysis The reporter ： Cui Yan 2012. 4. 26.

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Presentation transcript:

Feature extraction using fuzzy complete linear discriminant analysis The reporter ： Cui Yan

The report outlines 1.The fuzzy K-nearest neighbor classifier (FKNN) 2.The fuzzy complete linear discriminant analysis 3.Expriments

The Fuzzy K-nearest neighbor classifier (FKNN)

Each sample should be classified similarly to its surrounding samples, therefore, a unknown sample could be predicated by considering the classification of its nearest neighbor samples. The K-nearest neighbor classifier (KNN)

KNN tries to classify an unknown sample based on its k-known classification neighbors.

FKNN Given a sample set, a fuzzy M -class partition of these vectors specify the membership degrees of each sample corresponding to each class. The membership degree of a training vector to o each of M classes is specified by, which is computed by the following steps:

Step 1: Compute the distance matrix between pairs of feature vectors in the training. Step 2: Set diagonal elements of this matrix to infinity (practically place large numeric values there).

Step 3: Sort the distance matrix (treat each of its column separately) in an ascending order. Collect the class labels of the patterns located in the closest neigh- borhood of the pattern under consi- deration (as we are concerned with k neighbors, this returns a list of k integers).

Step 4: Compute the membership grade to class i for j-th pattern using the expression proposed in [1]. [1] J.M. Keller, M.R. Gray, J.A. Givens, A fuzzy k-nearest neighbor algorithm, IEEE Trans. Syst.Man Cybernet. 1985, 15(4):

A example for FKNN

Set k=3

The fuzzy complete linear discriminant analysis

For the training set, we define the i-th class mean by combining the fuzzy membership degree as And the total mean as (1) (2)

Incorporating the fuzzy membership degree, the between-class, the within-class and the total class fuzzy scatter matrix of samples can be defined as (3)

step1: Calculate the membership degree matrix U by the FKNN algorithm. step 2: According toEqs.(1)-(3) work out the between-class, within-class and total class fuzzy scatter matrices. step 3: Work out the orthogonal eigenvectors p1,..., pl of the total class fuzzy scatter matrix corresponding to positive eigenvalues. Algorithm of the fuzzy complete linear analysis

step 4: Let P = (p1,..., pl) and, work out the orthogonal eigenvectors g1,..., gr of correspending the zero eigenvalues. step 5: Let P1 = (g1,..., gr) and, work out the orthogonal eigenvectors v1,..., vr of, calculate the irregular discriminant vectors by.

step 6: Work out the orthogonal eigenvectors q1,…, qs of correspending the non-zero eigenvalues. step 7: Let P2 = (q1,…, qs) and, work out the optimal discriminant vectors vr+1,..., vr+s by the Fisher LDA, calculate the regular discriminant vectors by. step 8: (Recognition): Project all samples into the obtained optimal discriminant vectors and classify.

Experiments

We compare Fuzzy-CLDA with CLDA, UWLDA, FLDA, Fuzzy Fisherface, FIFDA on 3 different data sets from the UCI data sources. The characteristics of the three datasets can be found from ( All data sets are randomly split to the train set and test set with the ratio 1:4. Experiments are repeated 25 times to obtain mean prediction error rate as a performance measure, NCC is adopted to classify the test samples by using L2 norm.

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