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ICCV
2011
IEEE
12 years 9 months ago
Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction
Low-Rank Representation (LRR) [16, 17] is an effective method for exploring the multiple subspace structures of data. Usually, the observed data matrix itself is chosen as the dic...
Guangcan Liu, Shuicheng Yan
KDD
2006
ACM
179views Data Mining» more  KDD 2006»
14 years 9 months ago
Extracting key-substring-group features for text classification
In many text classification applications, it is appealing to take every document as a string of characters rather than a bag of words. Previous research studies in this area mostl...
Dell Zhang, Wee Sun Lee
MIR
2010
ACM
167views Multimedia» more  MIR 2010»
14 years 3 months ago
Improving automatic music classification performance by extracting features from different types of data
This paper discusses two sets of automatic musical genre classification experiments. Promising research directions are then proposed based on the results of these experiments. The...
Cory McKay, Ichiro Fujinaga
VISUALIZATION
1998
IEEE
14 years 1 months ago
Extremal feature extraction from 3-D vector and noisy scalar fields
We are interested in feature extraction from volume data in terms of coherent surfaces and 3-D space curves. The input can be an inaccurate scalar or vector field, sampled densely...
Chi-Keung Tang, Gérard G. Medioni
SAC
2006
ACM
14 years 3 months ago
The impact of sample reduction on PCA-based feature extraction for supervised learning
“The curse of dimensionality” is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimension...
Mykola Pechenizkiy, Seppo Puuronen, Alexey Tsymbal