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zhulemonjuice's Projects

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UnSupervised and Semi-Supervise Anomaly Detection / IsolationForest / KernelPCA Detection / ADOA / etc.

annotated_deep_learning_paper_implementations

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

anomaly-detection-resources

Anomaly detection related books, papers, videos, and toolboxes

anomaly-detection-via-oversampling-principal-component-analysis

Anomaly Detection via Over-sampling Principal Component Analysis

clustering

Clustering / Subspace Clustering Algorithms on MATLAB

correlation-based-subspace-anomaly-detection-framework-for-high-dimensional-data

Correlation Based Subspace Anomaly Detection Framework for High Dimensional Data

darts

A python library for user-friendly forecasting and anomaly detection on time series.

flow-forecast

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

grasta

GRASTA ( Grassmannian Robust Adaptive Subspace Tracking Algorithm ) for low rank subspace tracking

isvd-

An improved incremental singular value decomposition(SVD) algorithm

label

An Integrated Experimental Platform for time series data anomaly detection.

oadd-

Custom implementation of the DenStream algorithm in Python. The purpose is to detect anomalies applying the algorithm on Telemetry data coming from the devices.

online_pca

A collection of computationally efficient algorithms for online subspace learning and principal component analysis

os-k-means

现有聚类算法面向高维稀疏数据多未考虑类簇可重叠和离群点的存在，导致聚类效果不理想。针对此，提出一种可重叠子空间K-Means聚类算法(An Overlapping Subspace K-Means Clustering Algorithm, OS-K-Means)。给出类簇子空间计算策略，在聚类过程中动态更新每个类簇的属性子空间，并定义合理的约束函数指导聚类过程，从而实现类簇的可重叠性与寻找离群点的效果。具体地，定义合理的目标函数对传统的K-Means算法进行修正，利用熵权约束分别计算每个类簇中每个维度的权重，使用权重值来标识对不同类簇中维度的相对重要性，并加入对重叠程度和离群值数量控制的参数。

paddletimeseries

Awesome Easy-to-Use Deep Time Series Modeling based on PaddlePaddle, including comprehensive functionality modules like TSDataset, Analysis, Transform, Models, AutoTS, and Ensemble, etc., supporting versatile tasks like time series forecasting, representation learning, and anomaly detection, etc., featured with quick tracking of SOTA deep models.

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