Search: PCA - ai.jp.net

research #machine learning 📝 BlogAnalyzed: Jan 16, 2026 01:16

Pokemon Power-Ups: Machine Learning in Action!

Published:Jan 16, 2026 00:03

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1 min read

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Qiita ML

Analysis

This article offers a fun and engaging way to learn about machine learning! By using Pokemon stats, it makes complex concepts like regression and classification incredibly accessible. It's a fantastic example of how to make AI education both exciting and intuitive.

Key Takeaways

•Uses Pokemon stats (HP, Attack, Defense, etc.) to represent data.
•Covers a range of machine learning techniques including regression, classification, and unsupervised learning.
•Provides a creative and accessible entry point for learning about AI.

Reference

“Each Pokemon is represented by a numerical vector: [HP, Attack, Defense, Special Attack, Special Defense, Speed].”

Permalink Qiita ML

Research Paper #Recommender Systems, LLMs, Cognitive Architectures 🔬 ResearchAnalyzed: Jan 3, 2026 15:54

CogRec: A Cognitive Recommender Agent for Explainable Recommendations

Published:Dec 30, 2025 09:50

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1 min read

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ArXiv

Analysis

This paper addresses the limitations of Large Language Models (LLMs) in recommendation systems by integrating them with the Soar cognitive architecture. The key contribution is the development of CogRec, a system that combines the strengths of LLMs (understanding user preferences) and Soar (structured reasoning and interpretability). This approach aims to overcome the black-box nature, hallucination issues, and limited online learning capabilities of LLMs, leading to more trustworthy and adaptable recommendation systems. The paper's significance lies in its novel approach to explainable AI and its potential to improve recommendation accuracy and address the long-tail problem.

Key Takeaways

•Combines LLMs and Soar for explainable recommendations.
•Addresses limitations of LLMs like black-box nature and hallucination.
•Employs a Perception-Cognition-Action (PCA) cycle.
•Dynamically queries LLMs for solutions to impasses.
•Uses Soar's chunking for online learning and rule creation.
•Demonstrates advantages in accuracy, explainability, and long-tail problem solving.

Reference

“CogRec leverages Soar as its core symbolic reasoning engine and leverages an LLM for knowledge initialization to populate its working memory with production rules.”

Permalink ArXiv

Paper #Image Denoising 🔬 ResearchAnalyzed: Jan 3, 2026 16:03

Image Denoising with Circulant Representation and Haar Transform

Published:Dec 29, 2025 16:09

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1 min read

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ArXiv

Analysis

This paper introduces a computationally efficient image denoising algorithm, Haar-tSVD, that leverages the connection between PCA and the Haar transform within a circulant representation. The method's strength lies in its simplicity, parallelizability, and ability to balance speed and performance without requiring local basis learning. The adaptive noise estimation and integration with deep neural networks further enhance its robustness and effectiveness, especially under severe noise conditions. The public availability of the code is a significant advantage.

Key Takeaways

•Proposes a computationally efficient image denoising algorithm (Haar-tSVD).
•Establishes a theoretical connection between PCA and Haar transform under circulant representation.
•Employs a unified tensor singular value decomposition (t-SVD) projection with Haar transform.
•Offers a balance between denoising speed and performance without local basis learning.
•Includes an adaptive noise estimation scheme for improved robustness.
•Integrates deep neural networks to enhance performance under severe noise.
•Provides publicly available code.

Reference

“The proposed method, termed Haar-tSVD, exploits a unified tensor singular value decomposition (t-SVD) projection combined with Haar transform to efficiently capture global and local patch correlations.”

Permalink ArXiv

Research Paper #Diffusion Models, Inverse Problems, Machine Learning 🔬 ResearchAnalyzed: Jan 3, 2026 19:04

SURE Guided Posterior Sampling for Faster Inverse Problem Solving

Published:Dec 29, 2025 06:19

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1 min read

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ArXiv

Analysis

This paper introduces a novel method, SURE Guided Posterior Sampling (SGPS), to improve the efficiency of diffusion models for solving inverse problems. The core innovation lies in correcting sampling trajectory deviations using Stein's Unbiased Risk Estimate (SURE) and PCA-based noise estimation. This approach allows for high-quality reconstructions with significantly fewer neural function evaluations (NFEs) compared to existing methods, making it a valuable contribution to the field.

Key Takeaways

•Proposes SURE Guided Posterior Sampling (SGPS) to improve the efficiency of diffusion models for inverse problems.
•Uses SURE and PCA-based noise estimation to correct sampling trajectory deviations.
•Achieves high-quality reconstructions with significantly fewer neural function evaluations (NFEs).
•Outperforms existing methods at low NFE counts across diverse inverse problems.

Reference

“SGPS enables more accurate posterior sampling and reduces error accumulation, maintaining high reconstruction quality with fewer than 100 Neural Function Evaluations (NFEs).”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 28, 2025 15:02

When did you start using Gemini (formerly Bard)?

Published:Dec 28, 2025 12:09

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1 min read

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r/Bard

Analysis

This Reddit post on r/Bard is a simple question prompting users to share when they started using Google's AI model, now known as Gemini (formerly Bard). It's a basic form of user engagement and data gathering, providing anecdotal information about the adoption rate and user experience over time. While not a formal study, the responses could offer Google insights into user loyalty, the impact of the rebranding from Bard to Gemini, and potential correlations between usage start date and user satisfaction. The value lies in the collective, informal feedback provided by the community. It lacks scientific rigor but offers a real-time pulse on user sentiment.

Key Takeaways

•Simple user engagement question on Reddit.
•Provides anecdotal data on Gemini/Bard adoption.
•Potentially useful for Google to gauge user sentiment.

Reference

“submitted by /u/Short_Cupcake8610”

Permalink r/Bard

Research Paper #Quantum Physics, Computational Materials Science, Machine Learning 🔬 ResearchAnalyzed: Jan 4, 2026 00:19

Linear Foundation Model for Quantum Embedding: Accelerating Simulations of Strongly Correlated Materials

Published:Dec 25, 2025 13:17

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1 min read

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ArXiv

Analysis

This paper introduces a novel approach to accelerate quantum embedding (QE) simulations, a method used to model strongly correlated materials where traditional methods like DFT fail. The core innovation is a linear foundation model using Principal Component Analysis (PCA) to compress the computational space, significantly reducing the cost of solving the embedding Hamiltonian (EH). The authors demonstrate the effectiveness of their method on a Hubbard model and plutonium, showing substantial computational savings and transferability of the learned subspace. This work addresses a major computational bottleneck in QE, potentially enabling high-throughput simulations of complex materials.

Key Takeaways

•Introduces a linear foundation model for quantum embedding using PCA.
•Compresses the variational space, reducing computational cost.
•Demonstrates effectiveness on Hubbard model and plutonium.
•Enables high-throughput simulations of strongly correlated materials.

Reference

“The approach reduces each embedding solve to a deterministic ground-state eigenvalue problem in the reduced space, and reduces the cost of the EH solution by orders of magnitude.”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 25, 2025 22:26

[P] The Story Of Topcat (So Far)

Published:Dec 24, 2025 16:41

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1 min read

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r/MachineLearning

Analysis

This post from r/MachineLearning details a personal journey in AI research, specifically focusing on alternative activation functions to softmax. The author shares experiences with LSTM modifications and the impact of the Golden Ratio on tanh activation. While the findings are presented as somewhat unreliable and not consistently beneficial, the author seeks feedback on the potential merit of publishing or continuing the project. The post highlights the challenges of AI research, where many ideas don't pan out or lack consistent performance improvements. It also touches on the evolving landscape of AI, with transformers superseding LSTMs.

Key Takeaways

•Exploration of alternative activation functions in neural networks.
•Challenges in achieving consistent performance improvements in AI research.
•The rapid evolution of AI architectures (LSTMs vs. Transformers).

Reference

“A story about my long-running attempt to develop an output activation function better than softmax.”

Permalink r/MachineLearning

Research #Imaging 🔬 ResearchAnalyzed: Jan 10, 2026 10:22

Dimensionality Reduction Impact on Machine Learning in Hyperspectral Imaging

Published:Dec 17, 2025 15:51

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1 min read

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ArXiv

Analysis

This research article from ArXiv investigates the impact of Principal Component Analysis (PCA) for dimensionality reduction on machine learning performance in hyperspectral optical imaging. The study likely explores trade-offs between computational efficiency and accuracy when applying PCA.

Key Takeaways

•Investigates the use of PCA for dimensionality reduction.
•Focuses on hyperspectral optical imaging.
•Assesses the impact on machine learning performance.

Reference

“The research focuses on the effect of PCA-based dimensionality reduction.”

Permalink ArXiv

Research #LLM, PCA 🔬 ResearchAnalyzed: Jan 10, 2026 10:41

LLM-Powered Anomaly Detection in Longitudinal Texts via Functional PCA

Published:Dec 16, 2025 17:14

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1 min read

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ArXiv

Analysis

This research explores a novel application of Large Language Models (LLMs) in conjunction with Functional Principal Component Analysis (FPCA) for anomaly detection in sparse, longitudinal text data. The combination of LLMs for feature extraction and FPCA for identifying deviations presents a promising approach.

Key Takeaways

•The research proposes a method for anomaly detection in text using LLMs and FPCA.
•The approach specifically addresses sparse and longitudinal text data.
•The use of FPCA offers a potentially robust method for identifying unusual patterns.

Reference

“The article is sourced from ArXiv, indicating a pre-print research paper.”

Permalink ArXiv

Research #LLM 🔬 ResearchAnalyzed: Jan 10, 2026 11:05

SkipCat: Efficient Compression of Large Language Models for Resource-Constrained Environments

Published:Dec 15, 2025 16:25

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1 min read

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ArXiv

Analysis

The SkipCat paper presents a novel approach to compress large language models, targeting efficient deployment on resource-limited devices. Its focus on rank-maximized low-rank compression with shared projections and block skipping offers a promising direction for reducing model size and computational demands.

Key Takeaways

•SkipCat introduces a novel compression method for large language models.
•The approach uses shared projections and block skipping techniques.
•It aims to reduce computational and memory requirements for LLMs.

Reference

“SkipCat utilizes shared projection and block skipping for rank-maximized low-rank compression of large language models.”

Permalink ArXiv

Research #image processing 🔬 ResearchAnalyzed: Jan 4, 2026 10:20

Hyperspectral Image Data Reduction for Endmember Extraction

Published:Dec 11, 2025 10:27

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1 min read

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ArXiv

Analysis

This article likely discusses methods for reducing the dimensionality of hyperspectral image data while preserving the information needed for endmember extraction. This is a common problem in remote sensing and image processing, aiming to simplify data analysis and improve computational efficiency. The focus is on techniques that allow for the identification of pure spectral signatures (endmembers) within the complex hyperspectral data.

Key Takeaways

•Focus on dimensionality reduction techniques for hyperspectral images.
•Aims to improve endmember extraction efficiency and accuracy.
•Likely explores methods like PCA, ICA, or specialized algorithms.

Reference

“The article likely presents new algorithms or improvements to existing methods for dimensionality reduction, such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), or other techniques tailored for hyperspectral data.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:00

Classification EM-PCA for clustering and embedding

Published:Nov 24, 2025 11:18

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1 min read

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ArXiv

Analysis

This article, sourced from ArXiv, likely presents a novel method called Classification EM-PCA for data analysis tasks. The title suggests the method combines Expectation-Maximization (EM) with Principal Component Analysis (PCA) for clustering and embedding purposes. The focus is on a research paper, indicating a technical and potentially complex subject matter.

Key Takeaways

Reference

“”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 12:03

How Language Directions Align with Token Geometry in Multilingual LLMs

Published:Nov 16, 2025 16:36

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1 min read

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ArXiv

Analysis

This article likely explores the geometric relationships between language representations within multilingual Large Language Models (LLMs). It probably investigates how the directionality of different languages is encoded in the model's token space and how this geometry impacts the model's performance and understanding of different languages. The source being ArXiv suggests a focus on technical details and potentially novel findings.

Key Takeaways

•The article investigates the geometric relationships of language representations in multilingual LLMs.
•It likely explores how language directionality is encoded in the token space.
•The research may provide insights into how this geometry affects model performance and understanding.
•The source, ArXiv, suggests a focus on technical details and potentially novel findings.

Reference

“Without the full article, it's impossible to provide a specific quote. However, the article likely contains technical details about token embeddings, vector spaces, and potentially the use of techniques like Principal Component Analysis (PCA) or other dimensionality reduction methods to analyze the geometry.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Dec 25, 2025 04:49

What exactly does word2vec learn?

Published:Sep 1, 2025 09:00

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1 min read

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Berkeley AI

Analysis

This article from Berkeley AI discusses a new paper that provides a quantitative and predictive theory describing the learning process of word2vec. For years, researchers lacked a solid understanding of how word2vec, a precursor to modern language models, actually learns. The paper demonstrates that in realistic scenarios, the learning problem simplifies to unweighted least-squares matrix factorization. Furthermore, the researchers solved the gradient flow dynamics in closed form, revealing that the final learned representations are essentially derived from PCA. This research sheds light on the inner workings of word2vec and provides a theoretical foundation for understanding its learning dynamics, particularly the sequential, rank-incrementing steps observed during training.

Key Takeaways

•Word2vec learning can be understood through unweighted least-squares matrix factorization.
•The learning dynamics of word2vec involve discrete, sequential steps.
•Learned representations in word2vec are related to Principal Component Analysis (PCA).

Reference

“the final learned representations are simply given by PCA.”

Permalink Berkeley AI

Pokemon Power-Ups: Machine Learning in Action!

Analysis

Key Takeaways

CogRec: A Cognitive Recommender Agent for Explainable Recommendations

Analysis

Key Takeaways

Image Denoising with Circulant Representation and Haar Transform

Analysis

Key Takeaways

SURE Guided Posterior Sampling for Faster Inverse Problem Solving

Analysis

Key Takeaways

When did you start using Gemini (formerly Bard)?

Analysis

Key Takeaways

Linear Foundation Model for Quantum Embedding: Accelerating Simulations of Strongly Correlated Materials

Analysis

Key Takeaways

[P] The Story Of Topcat (So Far)

Analysis

Key Takeaways

Dimensionality Reduction Impact on Machine Learning in Hyperspectral Imaging

Analysis

Key Takeaways

LLM-Powered Anomaly Detection in Longitudinal Texts via Functional PCA

Analysis

Key Takeaways

SkipCat: Efficient Compression of Large Language Models for Resource-Constrained Environments

Analysis

Key Takeaways

Hyperspectral Image Data Reduction for Endmember Extraction

Analysis

Key Takeaways

Classification EM-PCA for clustering and embedding

Analysis

Key Takeaways

How Language Directions Align with Token Geometry in Multilingual LLMs

Analysis

Key Takeaways

What exactly does word2vec learn?

Analysis

Key Takeaways

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