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14,884
Tag recommendations based on tensor dimensionality reduction
- In RecSys ’08: Proc. of the ACM Conference on Recommender systems, 43–50
, 2008
"... Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize information items (songs, pictures, web links, products etc.). Collaborative tagging systems recommend tags to users based on what tags other users have used for the same items, aiming ..."
Abstract
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Cited by 54 (1 self)
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algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items and tags. These data is represented by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular
Recommending Posts in Political Blogs based on Tensor Dimensionality Reduction
"... Social Tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (posts, songs, pictures, web links, products etc.). Political blogs can recommend posts to users, based on tags they have in common with other similar users. However, a post in po ..."
Abstract
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Cited by 1 (0 self)
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, we apply Higher Order Singular Value Decomposition (HOSVD) on a 3-dimensional tensor to find latent semantic relationships between the three types of entities that exist in a social blogging system: users, posts, and tags. We perform experimental comparison of the proposed method against state
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
, 2003
"... One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a representation for data lying on a low-dimensional manifold embedded in a high-dimensional space. Drawing on the correspondenc ..."
Abstract
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Cited by 1226 (15 self)
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One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a representation for data lying on a low-dimensional manifold embedded in a high-dimensional space. Drawing
Nonlinear dimensionality reduction by locally linear embedding
- SCIENCE
, 2000
"... Many areas of science ..."
Laplacian eigenmaps and spectral techniques for embedding and clustering.
- Proceeding of Neural Information Processing Systems,
, 2001
"... Abstract Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami op erator on a manifold , and the connections to the heat equation , we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in ..."
Abstract
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Cited by 668 (7 self)
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in a higher dimensional space. The algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality preserving properties and a natural connection to clustering. Several applications are considered. In many areas of artificial intelligence, information
Mixtures of Probabilistic Principal Component Analysers
, 1998
"... Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a com ..."
Abstract
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Cited by 532 (6 self)
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of clustering, density modelling and local dimensionality reduction, and we demonstrate its applicat...
Analysis of Recommendation Algorithms for E-Commerce
, 2000
"... Recommender systems apply statistical and knowledge discovery techniques to the problem of making product recommendations during a live customer interaction and they are achieving widespread success in E-Commerce nowadays. In this paper, we investigate several techniques for analyzing large-scale pu ..."
Abstract
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Cited by 523 (22 self)
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-scale purchase and preference data for the purpose of producing useful recommendations to customers. In particular, we apply a collection of algorithms such as traditional data mining, nearest-neighbor collaborative ltering, and dimensionality reduction on two dierent data sets. The rst data set was derived from
A Comparative Study on Feature Selection in Text Categorization
, 1997
"... This paper is a comparative study of feature selection methods in statistical learning of text categorization. The focus is on aggressive dimensionality reduction. Five methods were evaluated, including term selection based on document frequency (DF), information gain (IG), mutual information (MI), ..."
Abstract
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Cited by 1320 (15 self)
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This paper is a comparative study of feature selection methods in statistical learning of text categorization. The focus is on aggressive dimensionality reduction. Five methods were evaluated, including term selection based on document frequency (DF), information gain (IG), mutual information (MI
The Vector Field Histogram -- Fast Obstacle Avoidance For Mobile Robots
- IEEE JOURNAL OF ROBOTICS AND AUTOMATION
, 1991
"... A new real-time obstacle avoidance method for mobile robots has been developed and implemented. This method, named the vector field histogram(VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. The VFH method uses a ..."
Abstract
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Cited by 484 (24 self)
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a two-dimensional Cartesian histogram gridas a world model. This world model is updated continuously with range data sampled by on-board range sensors. The VFH method subsequently employs a two-stage data-reduction process in order to compute the desired control commands for the vehicle
Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds
- Journal of Machine Learning Research
, 2003
"... The problem of dimensionality reduction arises in many fields of information processing, including machine learning, data compression, scientific visualization, pattern recognition, and neural computation. ..."
Abstract
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Cited by 385 (10 self)
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The problem of dimensionality reduction arises in many fields of information processing, including machine learning, data compression, scientific visualization, pattern recognition, and neural computation.
Results 1 - 10
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14,884
