Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Despite the success of Principal Component Analysis (PCA) for dimensionality reduction, it is known that its most expressive components do not necessarily represent important discriminant features for pattern recognition. , the problem of ranking PCA components, computed from multi-class databases, is addressed by building multiple linear learners that are combined through the AdaBoost.M2 in order to determine the discriminant contribution of each PCA feature. In our implementation, each learner is a akened version of a linear support vector machine (SVM). The strong learner built by the ensemble technique is processed following a strategy to get the global discriminant vector to sort PCA components according to their relevance for classification tasks. Also,  show how the proposed methodology to compute the global discriminant vector can be applied to other multi-class approaches, like the linear discriminant analysis (LDA). In the computational experiments  compare the obtained approaches with counterpart ones using facial expression experiments. Our experimental results have shown that the principal components selected by the proposed technique allows higher recognition rates using less linear features.
['Tiene A. Filisbino', 'Gilson A. Giraldi', 'Carlos Eduardo Thomaz']
I am writing a research paper on a idea Ranking Principal Components in Face Spaces through AdaBoost.M2 Linear Ensemble
[{"from": "human", "value": "I am writing a research paper on a idea Ranking Principal Components in Face Spaces through AdaBoost.M2 Linear Ensemble"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Despite the success of Principal Component Analysis (PCA) for dimensionality reduction, it is known that its most expressive components do not necessarily represent important discriminant features for pattern recognition. , the problem of ranking PCA components, computed from multi-class databases, is addressed by building multiple linear learners that are combined through the AdaBoost.M2 in order to determine the discriminant contribution of each PCA feature. In our implementation, each learner is a akened version of a linear support vector machine (SVM). The strong learner built by the ensemble technique is processed following a strategy to get the global discriminant vector to sort PCA components according to their relevance for classification tasks. Also,  show how the proposed methodology to compute the global discriminant vector can be applied to other multi-class approaches, like the linear discriminant analysis (LDA). In the computational experiments  compare the obtained approaches with counterpart ones using facial expression experiments. Our experimental results have shown that the principal components selected by the proposed technique allows higher recognition rates using less linear features."}]