- Title Pages
- Series Foreword
- Preface
-
1 Introduction to Semi-Supervised Learning -
1 A Taxonomy for Semi-Supervised Learning Methods -
3 Semi-Supervised Text Classification Using EM -
4 Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers -
5 Probabilistic Semi-Supervised Clustering with Constraints -
6 Transductive Support Vector Machines -
7 Semi-Supervised Learning Using Semi-Definite Programming -
8 Gaussian Processes and the Null-Category Noise Model -
9 Entropy Regularization -
10 Data-Dependent Regularization -
11 Label Propagation and Quadratic Criterion -
12 The Geometric Basis of Semi-Supervised Learning -
13 Discrete Regularization -
14 Semi-Supervised Learning with Conditional Harmonic Mixing -
15 Graph Kernels by Spectral Transforms -
16 Spectral Methods for Dimensionality Reduction -
17 Modifying Distances -
18 Large-Scale Algorithms -
19 Semi-Supervised Protein Classification Using Cluster Kernels -
20 Prediction of Protein Function from Networks -
25 Analysis of Benchmarks -
22 An Augmented PAC Model for Semi-Supervised Learning -
23 Metric-Based Approaches for Semi-Supervised Regression and Classification -
24 Transductive Inference and Semi-Supervised Learning -
25 A Discussion of Semi-Supervised Learning and Transduction - References
- Notation and Symbols
- Contributors
- Index
Large-Scale Algorithms
Large-Scale Algorithms
- Chapter:
- (p.332) (p.333) 18 Large-Scale Algorithms
- Source:
- Semi-Supervised Learning
- Author(s):
Delalleau Olivier
Bengio Yoshua
Le Roux Nicolas
- Publisher:
- The MIT Press
This chapter presents a subset selection method that can be used to reduce the original system to one of size m 〈〈 n. The idea is to solve for the labels of a subset S ⊂ X of only m points, while still retaining information from the rest of the data by approximating their label with a linear combination of the labels in S—using the induction formula presented in Chapter 11. This leads to an algorithm whose computational requirements scale as O(m2n) and memory requirements as O(m2), thus allowing one to take advantage of significantly bigger unlabeled data sets than with the original algorithms.
Keywords: subset selection method, linear combination, induction formula, algorithm, computational requirements, memory requirements, unlabeled data sets
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- Title Pages
- Series Foreword
- Preface
-
1 Introduction to Semi-Supervised Learning -
1 A Taxonomy for Semi-Supervised Learning Methods -
3 Semi-Supervised Text Classification Using EM -
4 Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers -
5 Probabilistic Semi-Supervised Clustering with Constraints -
6 Transductive Support Vector Machines -
7 Semi-Supervised Learning Using Semi-Definite Programming -
8 Gaussian Processes and the Null-Category Noise Model -
9 Entropy Regularization -
10 Data-Dependent Regularization -
11 Label Propagation and Quadratic Criterion -
12 The Geometric Basis of Semi-Supervised Learning -
13 Discrete Regularization -
14 Semi-Supervised Learning with Conditional Harmonic Mixing -
15 Graph Kernels by Spectral Transforms -
16 Spectral Methods for Dimensionality Reduction -
17 Modifying Distances -
18 Large-Scale Algorithms -
19 Semi-Supervised Protein Classification Using Cluster Kernels -
20 Prediction of Protein Function from Networks -
25 Analysis of Benchmarks -
22 An Augmented PAC Model for Semi-Supervised Learning -
23 Metric-Based Approaches for Semi-Supervised Regression and Classification -
24 Transductive Inference and Semi-Supervised Learning -
25 A Discussion of Semi-Supervised Learning and Transduction - References
- Notation and Symbols
- Contributors
- Index
