Results 1  10
of
814,013
NonNegative SemiSupervised Learning
"... The contributions of this paper are threefold. First, we present a general formulation for reaping the benefits from both nonnegative data factorization and semisupervised learning, and the solution naturally possesses the characteristics of sparsity, robustness to partial occlusions, and greater ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
The contributions of this paper are threefold. First, we present a general formulation for reaping the benefits from both nonnegative data factorization and semisupervised learning, and the solution naturally possesses the characteristics of sparsity, robustness to partial occlusions
SemiSupervised Learning Literature Survey
, 2006
"... We review the literature on semisupervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole
spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semisupervised learning. This document is a chapter ..."
Abstract

Cited by 757 (8 self)
 Add to MetaCart
We review the literature on semisupervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole
spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semisupervised learning. This document is a
SemiSupervised Learning Using Gaussian Fields and Harmonic Functions
 IN ICML
, 2003
"... An approach to semisupervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning ..."
Abstract

Cited by 741 (15 self)
 Add to MetaCart
An approach to semisupervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning
Semisupervised Clustering by Seeding
 In Proceedings of 19th International Conference on Machine Learning (ICML2002
, 2002
"... Semisupervised clustering uses a small amount of labeled data to aid and bias the clustering of unlabeled data. This paper explores the use of labeled data to generate initial seed clusters, as well as the use of constraints generated from labeled data to guide the clustering process. It intr ..."
Abstract

Cited by 206 (17 self)
 Add to MetaCart
Semisupervised clustering uses a small amount of labeled data to aid and bias the clustering of unlabeled data. This paper explores the use of labeled data to generate initial seed clusters, as well as the use of constraints generated from labeled data to guide the clustering process
Algorithms for Nonnegative Matrix Factorization
 In NIPS
, 2001
"... Nonnegative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown to minim ..."
Abstract

Cited by 1230 (5 self)
 Add to MetaCart
Nonnegative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown
Integrating Constraints and Metric Learning in SemiSupervised Clustering
 In ICML
, 2004
"... Semisupervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has utilized supervised data in one of two approaches: 1) constraintbased methods that guide the clustering algorithm towards a better grouping of the data, and 2) distanc ..."
Abstract

Cited by 245 (7 self)
 Add to MetaCart
Semisupervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has utilized supervised data in one of two approaches: 1) constraintbased methods that guide the clustering algorithm towards a better grouping of the data, and 2
Semisupervised Multilabel Learning by Constrained Nonnegative Matrix Factorization
, 2006
"... We present a novel framework for multilabel learning that explicitly addresses the challenge arising from the large number of classes and a small size of training data. The key assumption behind this work is that two examples tend to have large overlap in their assigned class memberships if they sh ..."
Abstract

Cited by 55 (1 self)
 Add to MetaCart
to the unlabeled data that minimizes the difference between these two sets of similarities. The optimization problem is formulated as a constrained Nonnegative Matrix Factorization (NMF) problem, and an algorithm is presented to efficiently find the solution. Compared to the existing approaches for multi
Supervised and SemiSupervised Separation of
, 2006
"... In this paper we describe a methodology for modelbased single channel separation of sounds. We present a sparse latent variable model that can learn sounds based on their distribution of time/frequency energy. This model can then be used to extract known types of sounds from mixtures in two scenari ..."
Abstract
 Add to MetaCart
scenarios. One being the case where all sound types in the mixture are known, and the other being being the case where only the target or the interference models are known. The model we propose has close ties to nonnegative decompositions and latent variable models commonly used for semantic analysis.
Semisupervised Learning by Entropy Minimization
"... We consider the semisupervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. This regularizer can be applied to ..."
Abstract

Cited by 101 (2 self)
 Add to MetaCart
We consider the semisupervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. This regularizer can be applied
Results 1  10
of
814,013