Unsupervised feature selection has become an important and challenging problem faced with vast amounts of unlabeled and high-dimension data in machine learning. We propose a novel unsupervised feature selection method...Unsupervised feature selection has become an important and challenging problem faced with vast amounts of unlabeled and high-dimension data in machine learning. We propose a novel unsupervised feature selection method using Structured Self-Representation( SSR) by simultaneously taking into account the selfrepresentation property and local geometrical structure of features. Concretely,according to the inherent selfrepresentation property of features,the most representative features can be selected. Mean while,to obtain more accurate results,we explore local geometrical structure to constrain the representation coefficients to be close to each other if the features are close to each other. Furthermore,an efficient algorithm is presented for optimizing the objective function. Finally,experiments on the synthetic dataset and six benchmark real-world datasets,including biomedical data,letter recognition digit data and face image data,demonstrate the encouraging performance of the proposed algorithm compared with state-of-the-art algorithms.展开更多
This paper introduces and analyzes several feature extraction algorithms. These algorithms use linear or non-linear feature extraction methods to project high-dimensional objects into lower dimensional space, thus the...This paper introduces and analyzes several feature extraction algorithms. These algorithms use linear or non-linear feature extraction methods to project high-dimensional objects into lower dimensional space, thus the complexity of the operations upon them, such as clustering, the nearest-neighbor search, visualization and etc can be reduced. The paper also presents some comparative experimental results of these algorithms and analyzes briefly their advantages or shortcomings.展开更多
基金Sponsored by the Major Program of National Natural Science Foundation of China(Grant No.13&ZD162)the Applied Basic Research Programs of China National Textile and Apparel Council(Grant No.J201509)
文摘Unsupervised feature selection has become an important and challenging problem faced with vast amounts of unlabeled and high-dimension data in machine learning. We propose a novel unsupervised feature selection method using Structured Self-Representation( SSR) by simultaneously taking into account the selfrepresentation property and local geometrical structure of features. Concretely,according to the inherent selfrepresentation property of features,the most representative features can be selected. Mean while,to obtain more accurate results,we explore local geometrical structure to constrain the representation coefficients to be close to each other if the features are close to each other. Furthermore,an efficient algorithm is presented for optimizing the objective function. Finally,experiments on the synthetic dataset and six benchmark real-world datasets,including biomedical data,letter recognition digit data and face image data,demonstrate the encouraging performance of the proposed algorithm compared with state-of-the-art algorithms.
文摘This paper introduces and analyzes several feature extraction algorithms. These algorithms use linear or non-linear feature extraction methods to project high-dimensional objects into lower dimensional space, thus the complexity of the operations upon them, such as clustering, the nearest-neighbor search, visualization and etc can be reduced. The paper also presents some comparative experimental results of these algorithms and analyzes briefly their advantages or shortcomings.
