The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Finding Local Linear Correlations in High Dimensional Data Xiang Zhang Feng Pan Wei Wang University of North Carolina at Chapel Hill Speaker: Xiang Zhang

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Finding Latent Patterns in High Dimensional Data An important research problem with wide applications  biology (gene expression analysis)  customer transactions, and so on. Common approaches  feature selection  feature transformation  subspace clustering

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Existing Approaches Feature selection  find a single representative subset of features that are most relevant for the data mining task at hand Feature transformation  find a set of new (transformed) features that contain the information in the original data as much as possible  Principal Component Analysis (PCA) Correlation clustering  find clusters of data points that may not exist in the axis parallel subspaces but only exist in the projected subspaces.

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Motivation Example Question: How to find these local linear correlations (using existing methods)? linearly correlated genes

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Applying PCA — Correlated? PCA is an effective way to determine whether a set of features is strongly correlated A global transformation applied to the entire dataset  a few eigenvectors describe most variance in the dataset  small amount of variance represented by the remaining eigenvectors  small residual variance indicates strong correlation

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Applying PCA – Representation? The linear correlation is represented as the hyperplane that is orthogonal to the eigenvectors with the minimum variances [1, -1, 1] linear correlations reestablished by full-dimensional PCA embedded linear correlations

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Applying Bi-clustering or Correlation Clustering Methods Correlation clustering  no obvious clustering structure Bi-clustering  no strong pair-wise correlations linearly correlated genes

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Revisiting Existing Work Feature selection  finds only one representative subset of features Feature transformation  performs one and the same feature transformation for the entire dataset  does not really eliminate the impact of any original attributes Correlation clustering  projected subspaces are usually found by applying standard feature transformation method, such as PCA

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Local Linear Correlations - formalization Idea: formalize local linear correlations as strongly correlated feature subsets  Determining if a feature subset is correlated  small residual variance  The correlation may not be supported by all data points -- noise, domain knowledge…  supported by a large portion of the data points

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Problem Formalization Suppose that F (m by n) be a submatrix of the dataset D (M by N) Let { } be the eigenvalues of the covariance matrix of F and arranged in ascending order F is strongly correlated feature subset if and(1)(2) total variance variance on the k eigenvectors having smallest eigenvalues (residual variance) number of supporting data points total number of data points

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Problem Formalization Suppose that F (m by n) be a submatrix of the dataset D (M by N) larger k, stronger correlation smaller ε, stronger correlation K and ε, together control the strength of the correlation Eigenvalue id Eigenvalues larger ksmaller ε

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Goal Goal: to find all strongly correlated feature subsets Enumerate all sub-matrices?  Not feasible (2 M×N sub-matrices in total)  Efficient algorithm needed Any property we can use?  Monotonicity of the objective function

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Monotonicity Monotonic w.r.t. the feature subset  If a feature subset is strongly correlated, all its supersets are also strongly correlated  Derived from Interlacing Eigenvalue Theorem  Allow us to focus on finding the smallest feature subsets that are strongly correlated  Enable efficient algorithm – no exhaustive enumeration needed

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL The CARE Algorithm Selecting the feature subsets  Enumerate feature subsets from smaller size to larger size (DFS or BFS)  If a feature subset is strongly correlated, then its supersets are pruned (monotonicity of the objective function)  Further pruning possible (refer to paper for details)

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Monotonicity Non-monotonic w.r.t. the point subset  Adding (or deleting) point from a feature subset can increase or decrease the correlation among the features  Exhaustive enumeration infeasible – effective heuristic needed

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL The CARE Algorithm Selecting the point subsets  Feature subset may only correlate on a subset of data points  If a feature subset is not strongly correlated on all data points, how to chose the proper point subset?

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL The CARE Algorithm Successive point deletion heuristic  greedy algorithm – in each iteration, delete the point that resulting the maximum increasing of the correlation among the subset of features  Inefficient – need to evaluate objective function for all data points

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL The CARE Algorithm Distance-based point deletion heuristic  Let S 1 be the subspace spanned by the k eigenvectors with the smallest eigenvalues  Let S 2 be the subspace spanned by the remaining n-k eigenvectors.  Intuition: Try to reduce the variance in S 1 as much as possible while retaining the variance in S 2  Directly delete (1-δ)M points having large variance in S 1 and small variance in S 2 (refer to paper for details)

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL The CARE Algorithm A comparison between two point deletion heuristics successivedistance-based

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Experimental Results (Synthetic) Linear correlation reestablished Full-dimensional PCACARE Linear correlation embedded

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Pair-wise correlations Linear correlation embedded (hyperplan representation) Experimental Results (Synthetic)

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Scalability evaluation Experimental Results (Synthetic)

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Experimental Results (Wage) Correlation clustering method & CARE CARE only A comparison between correlation clustering method and CARE (dataset (534×11)

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Experimental Results Linearly correlated genes (Hyperplan representations) (220 genes for 42 mouse strains) Nrg4: cell part Myh7: cell part; intracelluar part Hist1h2bk: cell part; intracelluar part Arntl: cell part; intracelluar part Nrg4: integral to membrane Olfr281: integral to membrane Slco1a1: integral to membrane P196867: N/A Oazin: catalytic activity Ctse: catalytic activity Mgst3: catalytic activity Hspb2: cellular physiological process L12Rik: cellular physiological process D01Rik: cellular physiological process P213651: N/A Ldb3: intracellular part Sec61g: intracellular part Exosc4: intracellular part BC048403: N/A Mgst3: catalytic activity; intracellular part Nr1d2: intracellular part; metal ion binding Ctse: catalytic activity Pgm3: metal ion binding Hspb2: cellular metabolism Sec61b: cellular metabolism Gucy2g: cellular metabolism Sdh1: cellular metabolism Ptk6: membrane Gucy2g: integral to membrane Clec2g: integral to membrane H2-Q2: integral to membrane

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Thank You ! Questions?