Margin Based Dimensionality Reduction and Generalization~!2010-03-17~!2010-05-26~!2010-08-23~!
نویسندگان
چکیده
منابع مشابه
Margin Based Dimensionality Reduction and Generalization
Linear discriminant analysis (LDA) for dimension reduction has been applied to a wide variety of problems such as face recognition. However, it has a major computational difficulty when the number of dimensions is greater than the sample size. In this paper, we propose a margin based criterion for linear dimension reduction that addresses the above problem associated with LDA. We establish an e...
متن کاملMax Margin Dimensionality Reduction
A fundamental problem in machine learning is to extract compact but relevant representations of empirical data. Relevance can be measured by the ability to make good decisions based on the representations, for example in terms of classification accuracy. Compact representations can lead to more human-interpretable models, as well as improve scalability. Furthermore, in multi-class and multi-tas...
متن کاملLarge-margin Weakly Supervised Dimensionality Reduction
This paper studies dimensionality reduction in a weakly supervised setting, in which the preference relationship between examples is indicated by weak cues. A novel framework is proposed that integrates two aspects of the large margin principle (angle and distance), which simultaneously encourage angle consistency between preference pairs and maximize the distance between examples in preference...
متن کاملLarge Margin Discriminant Dimensionality Reduction in Prediction Space
In this paper we establish a duality between boosting and SVM, and use this to derive a novel discriminant dimensionality reduction algorithm. In particular, using the multiclass formulation of boosting and SVM we note that both use a combination of mapping and linear classification to maximize the multiclass margin. In SVM this is implemented using a pre-defined mapping (induced by the kernel)...
متن کاملDiagnosis of Diabetes Using an Intelligent Approach Based on Bi-Level Dimensionality Reduction and Classification Algorithms
Objective: Diabetes is one of the most common metabolic diseases. Earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. Diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. Classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Open Artificial Intelligence Journal
سال: 2010
ISSN: 1874-0618
DOI: 10.2174/1874061801004010055