scholarly journals Multilabel Classification with Principal Label Space Transformation

2012 ◽  
Vol 24 (9) ◽  
pp. 2508-2542 ◽  
Author(s):  
Farbound Tai ◽  
Hsuan-Tien Lin
Keyword(s):  
Singular Value ◽  
Data Sets ◽  
Classification Problems ◽  
Real World Data ◽  
Multilabel Classification ◽  
Binary Relevance ◽  
Space Transformation ◽  
Empirical Performance ◽  
Value Decomposition ◽  
Novel Algorithm

We consider a hypercube view to perceive the label space of multilabel classification problems geometrically. The view allows us not only to unify many existing multilabel classification approaches but also design a novel algorithm, principal label space transformation (PLST), that captures key correlations between labels before learning. The simple and efficient PLST relies on only singular value decomposition as the key step. We derive the theoretical guarantee of PLST and evaluate its empirical performance using real-world data sets. Experimental results demonstrate that PLST is faster than the traditional binary relevance approach and is superior to the modern compressive sensing approach in terms of both accuracy and efficiency.

A NOVEL FLOW-BASED METHOD USING GREY RELATIONAL ANALYSIS FOR PATTERN CLASSIFICATION

2013 ◽  
Vol 12 (01) ◽  
pp. 75-93 ◽  
Author(s):  
YI-CHUNG HU
Keyword(s):  
Grey Relational Analysis ◽  
Learning Algorithm ◽  
Pairwise Comparison ◽  
The Other ◽  
Data Sets ◽  
Classification Problems ◽  
Real World Data ◽  
Relational Analysis ◽  
The Difference ◽  
Grey Relational

Flow-based methods based on the outranking relation theory are extensively used in multiple criteria classification problems. Flow-based methods usually employed an overall preference index representing the flow to measure the intensity of preference for one pattern over another pattern. A traditional flow obtained by the pairwise comparison may not be complete since it does not globally consider the differences on each criterion between all the other patterns and the latter. That is, a traditional flow merely locally considers the difference on each criterion between two patterns. In contrast with traditional flows, the relationship-based flow is newly proposed by employing the grey relational analysis to assess the flow from one pattern to another pattern by considering the differences on each criterion between all the other patterns and the latter. A genetic algorithm-based learning algorithm is designed to determine the relative weights of respective criteria to derive the overall relationship index of a pattern. Our method is tested on several real-world data sets. Its performance is comparable to that of other well-known classifiers and flow-based methods.

ALLEVIATING THE SPARSITY PROBLEM OF COLLABORATIVE FILTERING USING AN EFFICIENT ITERATIVE CLUSTERED PREDICTION TECHNIQUE

2012 ◽  
Vol 11 (01) ◽  
pp. 33-53 ◽  
Author(s):  
AMIRA ABDELWAHAB ◽  
HIROO SEKIYA ◽  
IKUO MATSUBA ◽  
YASUO HORIUCHI ◽  
SHINGO KUROIWA
Keyword(s):  
Collaborative Filtering ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Sparse Matrix ◽  
Hybrid Approach ◽  
Singular Value ◽  
Data Sets ◽  
Prediction Technique ◽  
Value Decomposition ◽  
Spectral Clustering Algorithm

Collaborative filtering (CF) is one of the most prevalent recommendation techniques, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. Although CF has been widely applied in various applications, its applicability is restricted due to the data sparsity, the data inadequateness of new users and new items (cold start problem), and the growth of both the number of users and items in the database (scalability problem). In this paper, we propose an efficient iterative clustered prediction technique to transform user-item sparse matrix to a dense one and overcome the scalability problem. In this technique, spectral clustering algorithm is utilized to optimize the neighborhood selection and group the data into users' and items' clusters. Then, both clustered user-based and clustered item-based approaches are aggregated to efficiently predict the unknown ratings. Our experiments on MovieLens and book-crossing data sets indicate substantial and consistent improvements in recommendations accuracy compared to the hybrid user-based and item-based approach without clustering, hybrid approach with k-means and singular value decomposition (SVD)-based CF. Furthermore, we demonstrated the effectiveness of the proposed iterative technique and proved its performance through a varying number of iterations.

TRANSDUCTIVE LEARNING FOR SHORT-TEXT CLASSIFICATION PROBLEMS USING LATENT SEMANTIC INDEXING

2005 ◽  
Vol 19 (02) ◽  
pp. 143-163 ◽  
Author(s):  
SARAH ZELIKOVITZ ◽  
FINELLA MARQUEZ
Keyword(s):  
Text Classification ◽  
Latent Semantic Indexing ◽  
Training Data ◽  
Data Sets ◽  
Semantic Indexing ◽  
Classification Problems ◽  
Transductive Learning ◽  
Short Text ◽  
Series Of Experiments ◽  
Value Decomposition

This paper presents work that uses Transductive Latent Semantic Indexing (LSI) for text classification. In addition to relying on labeled training data, we improve classification accuracy by incorporating the set of test examples in the classification process. Rather than performing LSI's singular value decomposition (SVD) process solely on the training data, we instead use an expanded term-by-document matrix that includes both the labeled data as well as any available test examples. We report the performance of LSI on data sets both with and without the inclusion of the test examples, and we show that tailoring the SVD process to the test examples can be even more useful than adding additional training data. This method can be especially useful to combat possible inclusion of unrelated data in the original corpus, and to compensate for limited amounts of data. Additionally, we evaluate the vocabulary of the training and test sets and present the results of a series of experiments to illustrate how the test set is used in an advantageous way.

scholarly journals Singular Value Decomposition-based Robust Cubature Kalman Filtering for an Integrated GPS/SINS Navigation System

2014 ◽  
Vol 68 (3) ◽  
pp. 549-562 ◽  
Author(s):  
Qiuzhao Zhang ◽  
Xiaolin Meng ◽  
Shubi Zhang ◽  
Yunjia Wang
Keyword(s):  
Kalman Filter ◽  
Singular Value Decomposition ◽  
Navigation System ◽  
Design Parameter ◽  
Singular Value ◽  
Design Parameters ◽  
Data Sets ◽  
Test Results ◽  
Cubature Kalman Filter ◽  
Value Decomposition

A new nonlinear robust filter is proposed in this paper to deal with the outliers of an integrated Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) navigation system. The influence of different design parameters for an H∞ cubature Kalman filter is analysed. It is found that when the design parameter is small, the robustness of the filter is stronger. However, the design parameter is easily out of step in the Riccati equation and the filter easily diverges. In this respect, a singular value decomposition algorithm is employed to replace the Cholesky decomposition in the robust cubature Kalman filter. With large conditions for the design parameter, the new filter is more robust. The test results demonstrate that the proposed filter algorithm is more reliable and effective in dealing with the outliers in the data sets produced by the integrated GPS/SINS system.

scholarly journals A Novel Based Recommended System Regularized with User Trust and Item Rating Prediction

2019 ◽  
pp. 59-67
Author(s):  
Anusha Viswanadapalli ◽  
Praveen Kumar Nelapati
Keyword(s):  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Rating Prediction ◽  
Value Decomposition ◽  
Matrix Factorization Technique ◽  
User Trust

Singular Value Decomposition (SVD) is trust-based matrix factorization technique for recommendations is proposed. Trust SVD integrates multiple information sources into the recommendation model to reduce the data sparsity and cold start problems and their deterioration of recommendation performance. An analysis of social trust data from four real-world data sets suggests that both the explicit and the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Trust SVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ uses the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted and trusting users on the guess of items for an active user. The proposed technique extends SVD++ with social trust information. Experimental results on the four data sets demonstrate that Trust SVD achieves accuracy than other recommendation techniques.

Reduced Order ARIMA Models of 2-D Terrain Profiles Using Singular Value Decomposition

2007 ◽  
Author(s):  
Shannon M. Wagner ◽  
John B. Ferris
Keyword(s):  
Singular Value Decomposition ◽  
Moving Average ◽  
Physical Characteristics ◽  
Singular Value ◽  
System Response ◽  
Data Sets ◽  
Vertical Excitation ◽  
The Road ◽  
Value Decomposition ◽  
Future Work

The terrain profile is the principal source of vertical excitation to a vehicle’s chassis. To correctly predict the system response an accurate model of the terrain is needed. It is impractical to simulate long data sets; therefore it is necessary to characterize roads so that they can be grouped into sets with similar physical characteristics. The first step is to consider the road to be a realization of an underlying stochastic process. This work develops a method for characterizing non-stationary terrain profiles though ARIMA (autoregressive integrated moving average) modeling and singular value decomposition techniques. It is proposed that the ARIMA coefficients and the distribution of the residual process are jointly dependent functions of the physical characteristics of road profiles. This dependence is then exploited by mapping these dependent functions onto a smaller set of random variables. The resulting number of coefficients required to characterize the terrain is greatly reduced. Examples demonstrate that non-stationary road profiles can be characterized in this manner. Future work in polynomial chaos and ARIMA modeling are discussed within the context of terrain characterization.

Sign in / Sign up

Export Citation Format

Share Document