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A Survey of Dimension Reduction Techniques
, 2002
"... this paper, we assume that we have n observations, each being a realization of the p dimensional random variable x = (x 1 , . . . , x p ) with mean E(x) = = ( 1 , . . . , p ) and covariance matrix E{(x )(x = # pp . We denote such an observation matrix by X = i,j : 1 p, 1 ..."
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Cited by 137 (0 self)
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this paper, we assume that we have n observations, each being a realization of the p dimensional random variable x = (x 1 , . . . , x p ) with mean E(x) = = ( 1 , . . . , p ) and covariance matrix E{(x )(x = # pp . We denote such an observation matrix by X = i,j : 1 p, 1 n}. If i and # i = # (i,i) denote the mean and the standard deviation of the ith random variable, respectively, then we will often standardize the observations x i,j by (x i,j i )/ # i , where i = x i = 1/n j=1 x i,j , and # i = 1/n j=1 (x i,j x i )
A review of dimension reduction techniques
, 1997
"... The problem of dimension reduction is introduced as a way to overcome the curse of the dimensionality when dealing with vector data in highdimensional spaces and as a modelling tool for such data. It is defined as the search for a lowdimensional manifold that embeds the highdimensional data. A cl ..."
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Cited by 42 (4 self)
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classification of dimension reduction problems is proposed. A survey of several techniques for dimension reduction is given, including principal component analysis, projection pursuit and projection pursuit regression, principal curves and methods based on topologically continuous maps, such as Kohonen’s maps
Comparing Dimension Reduction Techniques for Document Clustering
"... Abstract. In this research, a systematic study is conducted of four dimension reduction techniques for the text clustering problem, using five benchmark data sets. Of the four methods Independent Component Analysis (ICA), Latent Semantic Indexing (LSI), Document Frequency (DF) and Random Projectio ..."
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Abstract. In this research, a systematic study is conducted of four dimension reduction techniques for the text clustering problem, using five benchmark data sets. Of the four methods Independent Component Analysis (ICA), Latent Semantic Indexing (LSI), Document Frequency (DF) and Random
An Evaluation of Dimension Reduction Techniques for OneClass Classification
, 2007
"... Dimension reduction (DR) is important in the processing of data in domains such as multimedia or bioinformatics because such data can be of very high dimension. Dimension reduction in a supervised learning context is a well posed problem in that there is a clear objective of discovering a reduced re ..."
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Dimension reduction (DR) is important in the processing of data in domains such as multimedia or bioinformatics because such data can be of very high dimension. Dimension reduction in a supervised learning context is a well posed problem in that there is a clear objective of discovering a reduced
Dimension Reduction Techniques for Training Polynomial Networks
 in Proceedings of the Seventeenth International Conference on Machine Learning
, 2000
"... We propose two novel methods for reducing dimension ..."
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Cited by 4 (3 self)
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We propose two novel methods for reducing dimension
COMPARITIVE STUDY OF DIMENSION REDUCTION TECHNIQUES FOR MOOD DETECTION
"... Abstract: The expression recognition system is closely related to face recognition where a lot of research has been done and a vast array of algorithms has been introduced. The mood detection system can also be considered as a special case of a pattern recognition problem and many techniques are ava ..."
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Cited by 1 (1 self)
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Abstract: The expression recognition system is closely related to face recognition where a lot of research has been done and a vast array of algorithms has been introduced. The mood detection system can also be considered as a special case of a pattern recognition problem and many techniques
Empirical guidance on scatterplot and dimension reduction technique choices
 IEEE T Vis Comput Gr
, 2013
"... Abstract—To verify cluster separation in highdimensional data, analysts often reduce the data with a dimension reduction (DR) technique, and then visualize it with 2D Scatterplots, interactive 3D Scatterplots, or Scatterplot Matrices (SPLOMs). With the goal of providing guidance between these visua ..."
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Cited by 3 (0 self)
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Abstract—To verify cluster separation in highdimensional data, analysts often reduce the data with a dimension reduction (DR) technique, and then visualize it with 2D Scatterplots, interactive 3D Scatterplots, or Scatterplot Matrices (SPLOMs). With the goal of providing guidance between
A Novel Dimension Reduction Technique based on Correlation Coefficient
"... Abstract In this paper, a novel simple dimension reduction technique for classification is proposed based on correlation coefficient. Existing dimension reduction techniques like LDA is known for capturing the most discriminant features of the data in the projected space while PCA is known for pres ..."
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Abstract In this paper, a novel simple dimension reduction technique for classification is proposed based on correlation coefficient. Existing dimension reduction techniques like LDA is known for capturing the most discriminant features of the data in the projected space while PCA is known
A Novel Dimension Reduction Technique for 3D Capacitance Extraction of VLSI Interconnects
"... this paper, a new method named Dimension Reduction Technique (DRT) is presented ..."
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this paper, a new method named Dimension Reduction Technique (DRT) is presented
Comparing and Combining Dimension Reduction Techniques for Efficient Text Clustering
 Proceedings of the Workshop on Feature Selection for Data Mining, SIAM Data Mining, 2005
"... A great challenge of text mining arises from the increasingly large text datasets and the high dimensionality associated with natural language. In this research, a systematic study is conducted of six Dimension Reduction Techniques (DRT) in the context of the text clustering problem using three stan ..."
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Cited by 16 (1 self)
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A great challenge of text mining arises from the increasingly large text datasets and the high dimensionality associated with natural language. In this research, a systematic study is conducted of six Dimension Reduction Techniques (DRT) in the context of the text clustering problem using three
Results 1  10
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2,190,515