Decompose Learning: Combine Feature Extraction and Classification
In real world classification tasks, the original instances are represented by raw features. Usually domain related algorithms are needed to extract discriminative features. But the algorithms selection and additional parameters tuning are difficult for people with little domain knowledge and experience. In this paper, a new machine learning framework called decompose learning is proposed for classification tasks with raw features. The raw input space is decomposed into several subspaces and an independent base classifier is learned in each subspace. The predictive result of each base classifier can be regarded as a high-level feature of original task. The final classifier is learned on these high-level features. We model the decomposition as a subspace clustering problem and utilize target unrelated unlabeled data to extract target related subspaces. Besides that, we use a maximum margin base classifier selection strategy to do the capacity control. Empirical tests on MNIST and CaltechlOl datasets show that decompose learning can improve predictive accuracy considerably without highly specialized domain related feature extraction algorithms.
Classification SVM Model Selection Feature Extraction
Yang Yang Shanping Li
Dept. of Computer Science, Zhejiang University, Hangzhou, China
国际会议
深圳
英文
93-98
2011-03-28（万方平台首次上网日期，不代表论文的发表时间）