|Oracle® Data Mining Concepts
12c Release 1 (12.1)
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This chapter describes Singular Value Decomposition, an unsupervised algorithm used by Oracle Data Mining for feature extraction. See Chapter 9 for information about feature extraction.
This chapter contains these topics:
Singular Value Decomposition (SVD) and the closely-related Principal Component Analysis (PCA) are well established feature extraction methods that have a wide range of applications. Oracle Data Mining implements SVD as a feature extraction algorithm and PCA as a special scoring method for SVD models.
SVD and PCA are orthogonal linear transformations that are optimal at capturing the underlying variance of the data. This property is very useful for reducing the dimensionality of high-dimensional data and for supporting meaningful data visualization.
SVD and PCA have a number of important applications in addition to dimensionality reduction. These include matrix inversion, data compression, and the imputation of unknown data values.
SVD is a factorization method that decomposes a rectangular matrix X into the product of three matrices:
The values in S are called singular values. They are non-negative, and their magnitudes indicate the importance of the corresponding bases (components). The singular values reflect the amount of data variance captured by the bases. The first basis (the one with largest singular value) lies in the direction of the greatest data variance. The second basis captures the orthogonal direction with the second greatest variance, and so on.
SVD essentially performs a coordinate rotation that aligns the transformed axes with the directions of maximum variance in the data. This is a useful procedure under the assumption that the observed data has a high signal-to-noise ratio and that a large variance corresponds to interesting data content while a lower variance corresponds to noise.
SVD makes the assumption that the underlying data is Gaussian distributed and can be well described in terms of means and covariances.
To reduce dimensionality, SVD keeps lower-order bases (the ones with the largest singular values) and ignores higher-order bases (the ones with the smallest singular values). The rationale behind this strategy is that the low-order bases retain the characteristics of the data that contribute most to its variance and are likely to capture the most important aspects of the data.
Given a data set X (nxm), where n is the number of rows and m is the number of attributes, a low-rank SVD will use only k components (k ≤ min(m, n)). In typical implementations of SVD, the value of k requires a visual inspection of the ranked singular values associated with the individual components. In Oracle Data Mining, SVD automatically estimates the cutoff point, which corresponds to a significant drop in the explained variance.
SVD produces two sets of orthonormal bases (U and V). Either of these bases could be used as a new coordinate system. In Oracle Data Mining SVD, V is the new coordinate system, and U represents the projection of X in this coordinate system. The algorithm computes the projection of new data as follows:
where X (nxk) is the projected data in the reduced data space, defined by the first k components, and Vk and Sk define the reduced component set.
In Oracle Data Mining, SVD can process data sets with millions of rows and thousands of attributes. Oracle Data Mining automatically recommends an appropriate number of features, based on the data, for dimensionality reduction.
SVD has linear scalability with the number of rows and cubic scalability with the number of attributes when a full decomposition is computed. A low-rank decomposition is typically linear with the number of rows and linear with the number of columns. The scalability with the reduced rank depends on how the rank compares to the number of rows and columns. It can be linear when the rank is significantly smaller or cubic when it is on the same scale.
Several options are available for configuring the SVD algorithm. Among them are settings to control model size and performance, and whether to score with SVD projections or PCA projections.
The U matrix in SVD has as many rows as the number of rows in the build data. To avoid creating a large model, the U matrix is persisted only when an algorithm-specific setting is enabled. By default the U matrix is not persisted.
SVD can use approximate computations to improve performance. Approximation may be appropriate for data sets with many columns. An approximate low-rank decomposition provides good solutions at a reasonable computational cost. The quality of the approximation is dependent on the characteristics of the data. For data sets with more than 2500 attributes (the maximum number of features allowed) only approximate decomposition is possible.
SVD models can be configured to perform PCA projections. PCA is closely related to SVD. PCA computes a set of orthonormal bases (principal components) that are ranked by their corresponding explained variance. The main difference between SVD and PCA is that the PCA projection is not scaled by the singular values. The PCA projection to the new coordinate system is given by:
where (nxk) is the projected data in the reduced data space, defined by the first k components, and Vk defines the reduced component set.
See Also:DBMS_DATA_MINING, "Algorithm Constants and Settings: Singular Value Decomposition" in Oracle Database PL/SQL Packages and Types Reference
Automatic Data Preparation performs normalization for SVD. Additional data preparation for outlier treatment may be beneficial since SVD can be sensitive to outliers.
Missing value treatment is not needed, because Oracle Data Mining algorithms handle missing values automatically. SVD replaces random missing values with the mean. For sparse data (missing values in nested columns), SVD replaces missing values with zeros.