Also with the notion of similarity as seen in kernel tricks. What you might do to learn an index. Inducing a differential metric. Matrix factorisations and random features, high-dimensional statistics. Ultimately, this is always (at least implicitly) learning a manifold.
PCA and cousins
The classic. Kernel PCA, linear algebra and probabilistic formulations. Has a nice probabilistic interpretation “for free” via the Karhunen–Loève theorem.
Matrix factorisations are a mild generalisation here, from rank 1 operators to higher rank operators. 🏗
There are various extensions such as additive component analysis:
We propose Additive Component Analysis (ACA), a novel nonlinear extension of PCA. Inspired by multivariate nonparametric regression with additive models, ACA fits a smooth manifold to data by learning an explicit mapping from a low-dimensional latent space to the input space, which trivially enables applications like denoising.
Uniform Manifold approximation and projection for dimension reduction (McInnes, Healy, and Melville 2018). Apparently super hot right now. (HT James Nichols). Nikolay Oskolkov’s introduction is neat. John Baez discusses the category theoretic underpinning.
Stochastic neighbour embedding
Probabilistically preserving closeness. The height of this technique is the famous t-SNE, although as far as I understand it has been superseded by UMAP.
Autoencoder and word2vec
The “nonlinear PCA” interpretation of word2vec, I just heard from Junbin Gao.
\[L(x, x') = \|x-x\|^2=\|x-\sigma(U*\sigma*W^Tx+b)) + b')\|^2\]
For indexing my database
See Learnable indexes.
Locality Preserving projections
Try to preserve the nearness of points if they are connected on some (weight) graph.
So we seen an optimal projection vector.
(requirement for sparse similarity matrix?)
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