Sparsity preserving score for feature selection
نویسندگان
چکیده
منابع مشابه
Sparsity Score: a Novel Graph-Preserving Feature Selection Method
As thousands of features are available in many pattern recognition and machine learning applications, feature selection remains an important task to ̄nd the most compact representation of the original data. In the literature, although a number of feature selection methods have been developed, most of them focus on optimizing speci ̄c objective functions. In this paper, we ̄rst propose a general gr...
متن کاملFeature Selection, Sparsity, Regression Regularization
from Wikipedia A feature selection algorithm can be seen as the combination of a search technique for proposing new feature subsets, along with an evaluation measure which scores the different feature subsets. The simplest algorithm is to test each possible subset of features finding the one which minimizes the error rate. This is an exhaustive search of the space, and is computationally intrac...
متن کاملIterative sparsity score for feature selection and its extension for multimodal data
As a key dimensionality reduction technique in pattern recognition, feature selection has been widely used in information retrieval, text classification and genetic data analysis. In recent years, structural information contained in samples for guiding feature selection has become a new hot spot in machine learning field. Although tremendous feature selection methods have been developed, less i...
متن کاملLaplacian Score for Feature Selection
In supervised learning scenarios, feature selection has been studied widely in the literature. Selecting features in unsupervised learning scenarios is a much harder problem, due to the absence of class labels that would guide the search for relevant information. And, almost all of previous unsupervised feature selection methods are “wrapper” techniques that require a learning algorithm to eval...
متن کاملGeneralized Fisher Score for Feature Selection
Fisher score is one of the most widely used supervised feature selection methods. However, it selects each feature independently according to their scores under the Fisher criterion, which leads to a suboptimal subset of features. In this paper, we present a generalized Fisher score to jointly select features. It aims at finding an subset of features, which maximize the lower bound of tradition...
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: Applied Informatics
سال: 2015
ISSN: 2196-0089
DOI: 10.1186/s40535-015-0009-3