502 search results - page 6 / 101 » Principal Component Analysis for Sparse High-Dimensional Dat... |
KDD
2001
ACM
16 years 23 days ago
2001
ACM
The similarity join is an important operation for mining high-dimensional feature spaces. Given two data sets, the similarity join computes all tuples (x, y) that are within a dis...
113
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SIGMOD
2001
ACM
16 years 15 days ago
2001
ACM
The outlier detection problem has important applications in the eld of fraud detection, network robustness analysis, and intrusion detection. Most such applications are high dimen...
112
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COMPLIFE
2006
Springer
15 years 4 months ago
2006
Springer
We present a method for simultaneous dimension reduction and metastability analysis of high dimensional time series. The approach is based on the combination of hidden Markov model...
120
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SADM
2010
14 years 7 months ago
2010
This paper is concerned with classifying high dimensional data into one of two categories. In various settings, such as when dealing with fMRI and microarray data, the number of v...
128
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CORR
2010
Springer
15 years 14 days ago
2010
Springer
In this paper, we study the problem of recovering a low-rank matrix (the principal components) from a highdimensional data matrix despite both small entry-wise noise and gross spar...