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Multiple Features Data Set

Below are papers that cite this data set, with context shown. Papers were automatically harvested and associated with this data set, in collaboration with

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Xiaoli Z. Fern and Carla Brodley. Cluster Ensembles for High Dimensional Clustering: An Empirical Study. Journal of Machine Learning Research n, a. 2004.

High resolution computed tomography lung image data Dy et al. (1999) chart Synthetically generated control chart time series UCI KDD archive (Hettich and Bay, 1999) isolet6 Spoken letter recognition data set (6 letters only) UCI ML archive mfeat Handwritten digits represented by Fourier coefficients (Blake and Merz, 1998) satimage StatLog Satellite image data set (training set) segmentation Image

Jaakko Peltonen and Samuel Kaski. Discriminative Components of Data. IEEE. 2004.

whose properties are summarized in Table I. The Landsat, Isolet, and Multiple Features MFeat data sets are from UCI Machine Learning Repository [31], LVQ PAK refers to the Finnish acoustic phoneme data distributed with the LVQ-PAK [32], and TIMIT refers to phoneme data from the Darpa TIMIT acoustic

Xiaofeng He and Partha Niyogi. Locality Preserving Projections. NIPS. 2003.

obtained by PCA, while they are well separated in the principal direction obtained by LPP. 4.2. 2-D Data Visulization An experiment was conducted with the Multiple Features Database [3]. This dataset consists of features of handwritten numbers (`0'-`9') extracted from a collection of Dutch utility maps. 200 patterns per class (for a total of 2,000 patterns) have been digitized in binary images.

Simon Perkins and James Theiler. Online Feature Selection using Grafting. ICML. 2003.

were generated and the results shown are mean results. Dataset C is the Multiple Features database from the UCI repository. This is a handwritten digit recognition task, where digitized images of digits have been represented using 649 features of various

Pavel Paclik and Robert P W Duin and Geert M. P. van Kempen and Reinhard Kohlus. On Feature Selection with Measurement Cost and Grouped Features. Pattern Recognition Group, Delft University of Technology.

algorithms per orm sequential orward selection with criterion (1). 3 Experiments 3.1 Handwritten Digit Recognition In the first experiment, we use the proposed methods on the handwritten digit mfeat dataset rom [1]. The dataset contains 10 digit classes with 200 samples per class and six di.erent eature sets (649 eatures). In order to lower the computational requirements in this illustrative example,

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