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Chapter
Dimensionality Reduction and Topic Modeling: From Latent Semantic Indexing to Latent Dirichlet Allocation and Beyond
The bag-of-words representation commonly used in text analysis can be analyzed very efficiently and retains a great deal of useful information, but it is also troublesome because the same thought can be expres...
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Chapter and Conference Paper
Variational Graph Embedding for Globally and Locally Consistent Feature Extraction
Existing feature extraction methods explore either global statistical or local geometric information underlying the data. In this paper, we propose a general framework to learn features that account for both t...
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Chapter and Conference Paper
Sparse Kernel-Based Feature Weighting
The success of many learning algorithms hinges on the reliable selection or construction of a set of highly predictive features. Kernel-based feature weighting bridges the gap between feature extraction and su...
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Chapter and Conference Paper
Feature Selection by Nonparametric Bayes Error Minimization
This paper presents an algorithmic framework for feature selection, which selects a subset of features by minimizing the nonparametric Bayes error. A set of existing algorithms as well as new ones can be deriv...
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Chapter and Conference Paper
Efficient Feature Selection in the Presence of Outliers and Noises
Although regarded as one of the most successful algorithm to identify predictive features, Relief is quite vulnerable to outliers and noisy features. The recently proposed I-Relief algorithm addresses such defici...
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Chapter and Conference Paper
Fighting WebSpam: Detecting Spam on the Graph Via Content and Link Features
We address a novel semi-supervised learning strategy for Web Spam issue. The proposed approach explores graph construction which is the key of representing data semantical relationship, and emphasizes on label...
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Chapter and Conference Paper
Reformulated Parametric Learning Based on Ordinary Differential Equations
This paper presents a new parametric learning scheme, namely, Reformulated Parametric Learning (RPL). Instead of learning the parameters directly on the original model, this scheme reformulates the model into a s...
