Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a new discriminant analysis for feature extraction is derived from the perspective of least squares regression. To obtain great discriminative por beten classes, all the data points in each class are expected to be regressed to a single vector, and the basic task is to find a transformation matrix such that the squared regression error is minimized. To this end, two least squares discriminant analysis methods are developed under the orthogonal or the uncorrelated constraint.  show that the orthogonal least squares discriminant analysis is an extension to the null space linear discriminant analysis, and the uncorrelated least squares discriminant analysis is exactly equivalent to the traditional linear discriminant analysis. Comparative experiments show that the orthogonal one is more preferable for real world applications.
['Feiping Nie', 'Shiming Xiang', 'Yun Liu', 'Chenping Hou', 'Changshui Zhang']
I am writing a research paper on a idea Orthogonal vs. I am writing a research paper on a idea uncorrelated least squares discriminant analysis for feature extraction
[{"from": "human", "value": "I am writing a research paper on a idea Orthogonal vs. I am writing a research paper on a idea uncorrelated least squares discriminant analysis for feature extraction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a new discriminant analysis for feature extraction is derived from the perspective of least squares regression. To obtain great discriminative por beten classes, all the data points in each class are expected to be regressed to a single vector, and the basic task is to find a transformation matrix such that the squared regression error is minimized. To this end, two least squares discriminant analysis methods are developed under the orthogonal or the uncorrelated constraint.  show that the orthogonal least squares discriminant analysis is an extension to the null space linear discriminant analysis, and the uncorrelated least squares discriminant analysis is exactly equivalent to the traditional linear discriminant analysis. Comparative experiments show that the orthogonal one is more preferable for real world applications."}]