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kernels view markdown

An introduction to kernels and recent research.

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kernel basics

• basic definition
  • continuous
  • symmetric
  • PSD Gram matrix ($K_n = XX^T$)
• list of kernels
• kernels wiki: kernel memorizes points then uses dists between points to classify
• learning deep kernels
• learning data-adaptive kernels
• kernels that mimic dl
• kernel methods
• wavelet support vector machines - kernels using wavelets

ch 4 from support vector machines book

• 4.1 - what is a valid kernel
  • in general, most dot-product like things constitute valid kernels
  • a function is a kernel iff it is a symmetric, positive definite function
    • this refers to the $n$ x $n$ matrix with entries $f(x_{row}-x_{col})$ being a psd matrix
  • a given kernel can have many feature spaces (can construct different feature spaces that yield the same inner products)
• 4.2 reproducing kernel hilbert space (RKHS) of a kernel
  • hilbert space - abstract vector space with (1) an inner product and (2) is complete (i.e. enough limits so calculus works)
  • the RKHS is the smallest feature space of a kernel, and can serve as a canonical feature space
    • RKHS - a $\mathbb K$-hilbert space that consists of functions mapping from X to $\mathbb K$
    • every RKHS has a unique reproducing kernel
    • every kernel has a unique RKHS
  • sums/products of kernels also work

kernel papers

• data spectroscopy paper (shi et al. 2009)
  • kernel matrix $K_n$
  • Laplacian matrix $L_n = D_n - K_n$
    • $D_n$ is diagonal matrix, with entries = column sums
  • block-diagonal kernel matrix would imply a cluster
    • eigenvalues of these kernel matrices can identify clusters (and are invariant to permutation)
    • want to look for data points corresponding to same/similar eigenvectors
  • hard to know what kernel to use, how many eigenvectors / groups look at
  • here, look at population point of view - realted dependence of spectrum of $K_n$ on the data density function: $K_Pf(x) = \int K(x, y) f(y) dP(y)$

spectral clustering

• interested in top eigenvectors of $K_n$ and bottom eigenvectors of $L_n$
• scott and longuet-higgins - embed data in space of top eigenvectors, normalize in that space, and investigate block structure
• perona and freeman - 2 clusters by thresholding top eigenvector
• shi & malik - normalized cut: threshold second smallest generalize eigenvector of $L_n$
• similarly we have kernel PCA, spectral dimensionality reduction, and SVMs (which can be viewed as fitting a linear classifier in the eigenspace of $K_n$)

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