NDLI: DSP: Robust Semi-supervised Dimensionality Reduction Using Dual Subspace Projections

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DSP: Robust Semi-supervised Dimensionality Reduction Using Dual Subspace Projections

Probabilistic Metrics for Soft-Clustering and Topic Model Validation

Tag Allocation Model: Model Noisy Social Annotations by Reason Finding

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Communication Structure Discovery via Information Asymmetry in an Organizational Social Network

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DSP: Robust Semi-supervised Dimensionality Reduction Using Dual Subspace Projections

Content Provider	IEEE Xplore Digital Library
Author	Su Yan Bouaziz, S. Dongwon Lee
Copyright Year	2010
Abstract	High-dimensional data usually incur learning deficiencies and computational difficulties. We present a novel semi-supervised dimensionality reduction technique that embeds high-dimensional data in an optimal low-dimensional subspace, which is learned with a few user supplied constraints as well as the structure of input data. We study two types of constraints that indicate whether or not pairs of data points originate from the same class. Data partitions that satisfy both types of constraints may be conflicting. To solve this problem, our method projects data into two different subspaces, one in the kernel space and one in the original input space, each is designed for enforcing one type of constraints. Projections in the two spaces interact and data are embedded in an optimal low-dimensional subspace where constraints are maximally satisfied. Besides constraints, our method also preserves the intrinsic data structure, such that nearby/far away data points in the original space are still near to/far from each other in the embedded space. Compared to existing techniques, our method has the following advantages: 1) It can benefit from constraints even when only a few are available. 2) It is robust and does not suffer from over fitting. 3) It handles nonlinearly separable data, but learns a linear data transformation. Thus the method can be easily generalized to new data points and is efficient in dealing with large data sets. Experiments on real data from multiple domains clearly demonstrate that significant improvements in learning accuracy can be achieved after dimensionality reduction by employing only a few constraints.
Starting Page	398
Ending Page	405
File Size	349206
Page Count	8
File Format	PDF
ISBN	9781424484829
DOI	10.1109/WI-IAT.2010.182
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article

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