Learning Collaborative Sparsity Structure via Nonconvex Optimization for Feature Recognition

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Transfer Learning in Collaborative Filtering for Sparsity Reduction Via Feature Tags Learning Model

Recently, many scholars have proposed recommendation models to alleviate the sparsity problem by transferring rating matrix in other domains. But different domains have different rating scales. Simple process for the rating scale does not reflect the real situation. The diversity of rating scales may cause the opposite effect, making the recommendation results more imprecise. In this paper, we ...

متن کامل

Efficient Sparse Group Feature Selection via Nonconvex Optimization

Sparse feature selection has been demonstrated to be effective in handling high-dimensional data. While promising, most of the existing works use convex methods, which may be suboptimal in terms of the accuracy of feature selection and parameter estimation. In this paper, we expand a nonconvex paradigm to sparse group feature selection, which is motivated by applications that require identifyin...

متن کامل

Image alignment via kernelized feature learning

Machine learning is an application of artificial intelligence that is able to automatically learn and improve from experience without being explicitly programmed. The primary assumption for most of the machine learning algorithms is that the training set (source domain) and the test set (target domain) follow from the same probability distribution. However, in most of the real-world application...

متن کامل

Multi-View Clustering and Feature Learning via Structured Sparsity

Combining information from various data sources has become an important research topic in machine learning with many scientific applications. Most previous studies employ kernels or graphs to integrate different types of features, which routinely assume one weight for one type of features. However, for many problems, the importance of features in one source to an individual cluster of data can ...

متن کامل

Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction

We propose a stochastic variance reduced optimization algorithm for solving sparse learning problems with cardinality constraints. Sufficient conditions are provided, under which the proposed algorithm enjoys strong linear convergence guarantees and optimal estimation accuracy in high dimensions. We further extend the proposed algorithm to an asynchronous parallel variant with a near linear spe...

متن کامل

ذخیره 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: IEEE Transactions on Industrial Informatics

سال: 2018

ISSN: 1551-3203,1941-0050

DOI: 10.1109/tii.2017.2777144