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By Sabu M. Thampi, Alexander Gelbukh, Jayanta Mukhopadhyay
This edited quantity includes a collection of refereed and revised papers initially provided on the overseas Symposium on sign Processing and clever acceptance platforms (SIRS-2014), March 13-15, 2014, Trivandrum, India. this system committee got 134 submissions from eleven international locations. every one paper used to be peer reviewed through a minimum of 3 or extra autonomous referees of this system committee and the fifty two papers have been ultimately chosen. The papers provide stimulating insights into development popularity, computer studying and Knowledge-Based structures sign and Speech Processing snapshot and Video Processing cellular Computing and purposes and computing device imaginative and prescient. The booklet is directed to the researchers and scientists engaged in numerous box of sign processing and similar components.
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6% accuracy obtained by applying k-nearest neighbour method directly. The method was also successfully demonstrated to solve complex pattern recognition problems on 2-D image problems through the testing of the AR face database. The method generated an accuracy of 92% on the AR database as opposed to 70% achieved with using the global distance nearest neighbour classifier. We also establish that distance and similarity measures generate different outcomes and that the similarity measures was observed to work better than distance measure when used with linear operators.
However, the features possess different scales and importance which are useful for classification. Keeping this in mind, An and Ruan  proposed Enhanced FLD (EFLD) method in which the scale of features Φ j (j = 1, 2,…, n) was used to find their importance and is defined as: Φj = 1 N −1 N −1 (x ( j) i − μ( j) )2 (5) i =1 Here μ ( j ) is mean of feature j. , Φ n ) and α is a scale parameter which controls the effect of features and ranges between (0, ∞) . Sw being singular due to limited samples, the optimal transformation is found by first reducing the dimension using Enhanced PCA (EPCA)  and then applying EFLD as follows: Wopt = Wepca Wefld where Wepca = Wefld = (7) arg max T −α W Λ S t ( Λ −α )T W and W −α T T −α arg max W Λ S b ( Λ ) W − α T W W Λ S ( Λ −α )T W w Exponential Discriminant Analysis (EDA) Zhang et al.
38 Rank 2 3 5 4 6 9 7 1 8 Pj = 1 n k × ni nk ni p k ij (11) k =1 i =1 where nk is the number of datasets and ni is the number of instances compared (nk = 3 and ni = 7 in our experiments). The methods investigated here are ranked based on the value of Pj ( j = 1,2,…,9) where rank 1 is given to the method which performs the best among all the method and rank 9 is given to the method performing worst as given in Table 5. A Comparative Study of Linear Discriminant and Linear Regression 31 We observe the following from Tables 2-5: 1.
Advances in Signal Processing and Intelligent Recognition Systems by Sabu M. Thampi, Alexander Gelbukh, Jayanta Mukhopadhyay