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Dimensionality Reduction

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하위 태스크: 2
평가표: 2
보존된 평가 기록: 6

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

<span style="color:grey; opacity: 0.6">( Image credit: openTSNE )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/schmidtjonathan/rrkf.jl

공식 코드

The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions

https://github.com/schmidtjonathan/rrkf_experiments

공식 코드

The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions

https://github.com/scottgigante/m-phate

공식 코드

Visualizing the PHATE of Neural Networks

https://github.com/seanfcottrell/plpca

공식 코드

PLPCA: Persistent Laplacian Enhanced-PCA for Microarray Data Analysis

https://github.com/selflein/ma-ebm

공식 코드

Master's Thesis: Out-of-distribution Detection with Energy-based Models

https://github.com/senyuan-juncheng/TensorProjectionLayer

공식 코드

TensorProjection Layer: A Tensor-Based Dimension Reduction Method in Deep Neural Networks

https://github.com/seyedmuhammadhosseinmousavi/introduction-to-facial-micro-expressions-analysis-using-color-and-depth-images-a-matlab-coding-appr

공식 코드

Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

https://github.com/sgh14/deep-diffusion-maps

공식 코드

Deep Diffusion Maps

https://github.com/shahsohil/DCC

공식 코드

Deep Continuous Clustering

https://github.com/shallowlearner93/neurodavis

공식 코드

NeuroDAVIS: A neural network model for data visualization

https://github.com/shashankkotyan/k-Distribution

공식 코드

k* Distribution: Evaluating the Latent Space of Deep Neural Networks using Local Neighborhood Analysis

https://github.com/shefaliqamar/hiv-epistatic

공식 코드

Identifying Selections Operating on HIV-1 Reverse Transcriptase via Uniform Manifold Approximation and Projection

https://github.com/sheqi/GP-RNN_UAI2019

공식 코드

Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks

https://github.com/sidchaini/distclassipy

공식 코드

Light Curve Classification with DistClassiPy: a new distance-based classifier

https://github.com/sidchaini/lightcurvedistanceclassification

공식 코드

Light Curve Classification with DistClassiPy: a new distance-based classifier

https://github.com/sjirschielen/groupicaonabide

공식 코드

ICA-based Resting-State Networks Obtained on Large Autism fMRI Dataset ABIDE

https://github.com/sjkoelle/manifoldflasso_jmlr

공식 코드

Manifold Coordinates with Physical Meaning

https://github.com/slautin/2024_co-orchestration

공식 코드

Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries

https://github.com/slinderman/pyglm

공식 코드

Bayesian latent structure discovery from multi-neuron recordings

https://github.com/sloooWTYK/DRiLLS

공식 코드

Level set learning with pseudo-reversible neural networks for nonlinear dimension reduction in function approximation

https://github.com/smashhadi/feature-selection-extraction

공식 코드

Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review

https://github.com/smortezah/napr

공식 코드

The chemical space of terpenes: insights from data science and AI

https://github.com/sorul/xnb

공식 코드

XNB: Explainable Class-Specific NaIve-Bayes Classifier

https://github.com/spectraldani/UnscentedGPLVM

공식 코드

Learning GPLVM with arbitrary kernels using the unscented transformation

https://github.com/sphinxteam/StructuredPrior_demo

공식 코드

The spiked matrix model with generative priors

https://github.com/srijithbalakrishnan/dreaminsg-integrated-model

공식 코드

Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models

https://github.com/ssilwa/optml

공식 코드

Using Dimensionality Reduction to Optimize t-SNE

https://github.com/sskoul/ID2216

공식 코드

Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent

https://github.com/staoxiao/retromae

공식 코드

RetroMAE v2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models

https://github.com/stat-ml/ncvis

공식 코드

NCVis: Noise Contrastive Approach for Scalable Visualization

https://github.com/steverab/failing-loudly

공식 코드

Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift

https://github.com/sxzhang25/product-manifold-learning

공식 코드

Product Manifold Learning

https://github.com/sydney-machine-learning/BayesianAutoencoders

공식 코드

Revisiting Bayesian Autoencoders with MCMC

https://github.com/sydney-machine-learning/autoencoders_remotesensing

공식 코드

Remote sensing framework for geological mapping via stacked autoencoders and clustering

https://github.com/takanori-fujiwara/cmca

공식 코드

Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political Parties

https://github.com/takuyaisomura/predpca

공식 코드

Dimensionality reduction to maximize prediction generalization capability

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.