Applying dimensional reduction techniques to Kepler data.
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Updated
Jan 23, 2018 - Jupyter Notebook
Applying dimensional reduction techniques to Kepler data.
A comparison between some dimension reduction algorithms
Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)
Mirror of the Bioconductor package CellTrails (http://bioconductor.org/packages/CellTrails/)
In this repo, I demonstrate how simple Linear Algebra concepts can be utilized for powerful image element detection applications
Implemented Laplacian Eigenmaps
Measure the distance between two spectra/signals using optimal transport and related metrics
Clustering exploration using the authors dataset
Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can…
This project focuses on network anomaly detection due to the exponential growth of network traffic and the rise of various anomalies such as cyber attacks, network failures, and hardware malfunctions. This project implement clustering algorithms from scratch, including K-means, Spectral Clustering, Hierarchical Clustering, and DBSCAN
Python implementation of network spectral embedder.
Sklearn, PCA, t-SNE, Isomap, NMF, Random Projection, Spectral Embedding
This project aims to compare the performance obtained using a linear Support Vector Machine model whose data was first processed through a Shortest Path kernel with the same SVM, this time with data also processed by two alternative Manifold Learning techniques: Isomap and Spectral Embedding.
Basis invariance synthetic experiment in Appendix D of NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
Knowledge-Based Systems, Wang, Chenchen, Zhichen Gu, and Jin-Mao Wei. "Spectral clustering and embedding with inter-class topology-preserving." 2024.
Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
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