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        "author": "Andreas Hinterreiter",
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        "description": "# kerneloose\n\nPython implementation of the kernel method for out-of-sample extension (OOSE) of dimensionality reduction techniques.\n\nBased on [\"Parametric nonlinear dimensionality reduction using kernel t-SNE\" by Gisbrecht, Schulz, and Hammer](https://www.sciencedirect.com/science/article/pii/S0925231214007036).\n\nThe kernel method is particularly useful for projection techniques that are computationally expensive and/or have non-convex objective functions, such as [t-SNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html).\n\n## Installation\n\n```\npip install kerneloose\n```\n\n## Usage example\n\nThe syntax follows scikit learn conventions.\nAssume `hd_data` is a numpy array containing high-dimensional data, and an array `ld_data` of equal length but lower dimension was obtained by some projection technique.\nAn OOSE of that projection can be obtained by:\n\n```python\nfrom kerneloose import KernelMap\n\nkernel_oose = KernelMap()\nkernel_oose.fit(hd_data, ld_data)\n```\n\nThe mapping can be applied to `new_data` (with same dimensionality as `ld_data`) simply by:\n\n```python\nkernel_oose.transform(new_data)\n```\n\nParameters of the calculated OOSE mapping can be saved and loaded for later use:\n\n```python\nkernel_oose.save('some/file/name')\n\nresume_later = KernelMap()\nresume_later.load('some/file/name')\n```\n\n\n",
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