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Found 10 papers, 3 papers with code
Improved Algorithm and Bounds for Successive Projection
Vertex hunting is the problem of estimating the $K$ vertices of the simplex.
Recent Advances in Text Analysis
In particular, we propose a new statistical model for ranking the citation impacts of $11$ topics, and we also build a cross-topic citation graph to illustrate how research results on different topics spread to one another.
Subject clustering by IF-PCA and several recent methods
We also find that IF-PCA is quite competitive, which slightly outperforms Seurat and SC3 over the $8$ single-cell data sets.
Phase transition for detecting a small community in a large network
Using a degree-corrected block model (DCBM), we establish phase transitions of this testing problem concerning the size of the small community and the edge densities in small and large communities.
Testing High-dimensional Multinomials with Applications to Text Analysis
Motivated by applications in text mining and discrete distribution inference, we investigate the testing for equality of probability mass functions of $K$ groups of high-dimensional multinomial distributions.
Measurement error models: from nonparametric methods to deep neural networks
The success of deep learning has inspired recent interests in applying neural networks in statistical inference.
Improvements on SCORE, Especially for Weak Signals
It accommodates severe degree heterogeneity and is adaptive to different levels of sparsity, but its performance for networks with weak signals is unclear.
State Aggregation Learning from Markov Transition Data
Our proposed method is a simple two-step algorithm: The first step is spectral decomposition of empirical transition matrix, and the second step conducts a linear transformation of singular vectors to find their approximate convex hull.
A Geometrical Approach to Topic Model Estimation
In the probabilistic topic models, the quantity of interest---a low-rank matrix consisting of topic vectors---is hidden in the text corpus matrix, masked by noise, and the Singular Value Decomposition (SVD) is a potentially useful tool for learning such a low-rank matrix.
Phase Transitions for High Dimensional Clustering and Related Problems
In the two-dimensional phase space calibrating the rarity and strengths of useful features, we find the precise demarcation for the Region of Impossibility and Region of Possibility.
