Semi-Supervised Learning via Geodesic Weighted Sparse Representation

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

Semi-supervised Learning by Sparse Representation

In this paper, we present a novel semi-supervised learning framework based on `1 graph. The `1 graph is motivated by that each datum can be reconstructed by the sparse linear superposition of the training data. The sparse reconstruction coefficients, used to deduce the weights of the directed `1 graph, are derived by solving an `1 optimization problem on sparse representation. Different from co...

متن کامل

Online Semi-Supervised Discriminative Dictionary Learning for Sparse Representation

We present an online semi-supervised dictionary learning algorithm for classification tasks. Specifically, we integrate the reconstruction error of labeled and unlabeled data, the discriminative sparse-code error, and the classification error into an objective function for online dictionary learning, which enhances the dictionary’s representative and discriminative power. In addition, we propos...

متن کامل

Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning

Constructing an informative and discriminative graph plays an important role in various pattern recognition tasks such as clustering and classification. Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL). In SSL, the graph is composed of both labele...

متن کامل

Semi-supervised Clustering for Short Text via Deep Representation Learning

In this work, we propose a semi-supervised method for short text clustering, where we represent texts as distributed vectors with neural networks, and use a small amount of labeled data to specify our intention for clustering. We design a novel objective to combine the representation learning process and the kmeans clustering process together, and optimize the objective with both labeled data a...

متن کامل

Semi-supervised Data Representation via Affinity Graph Learning

We consider the general problem of utilizing both labeled and unlabeled data to improve data representation performance. A new semi-supervised learning framework is proposed by combing manifold regularization and data representation methods such as Non negative matrix factorization and sparse coding. We adopt unsupervised data representation methods as the learning machines because they do not ...

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: IEICE Transactions on Information and Systems

سال: 2014

ISSN: 0916-8532,1745-1361

DOI: 10.1587/transinf.e97.d.1673