Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Orthogonal Nonnegative Matrix Factorization (ONMF) aims to approximate a nonnegative matrix as the product of two k-dimensional nonnegative factors, one of which has orthonormal columns. It yields potentially useful data representations as superposition of disjoint parts, while it has been shown to work ll for clustering tasks where traditional methods underperform. Existing algorithms rely mostly on heuristics, which despite their good empirical performance, lack provable performance guarantees.#R##N##R##N# present a new ONMF algorithm with provable approximation guarantees. For any constant dimension k,  obtain an additive EPTAS without any assumptions on the input. Our algorithm relies on a novel approximation to the related Non-negative Principal Component Analysis (NNPCA) problem; given an arbitrary data matrix, NNPCA seeks k nonnegative components that jointly capture most of the variance. Our NNPCA algorithm is of independent interest and generalizes previous work that could only obtain guarantees for a single component.#R##N##R##N# evaluate our algorithms on several real and synthetic datasets and show that their performance matches or outperforms the state of the art.
['Megasthenis Asteris', 'Dimitris S. Papailiopoulos', 'Alexandros G. Dimakis']
I am writing a research paper on a idea Orthogonal NMF through subspace exploration
[{"from": "human", "value": "I am writing a research paper on a idea Orthogonal NMF through subspace exploration"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Orthogonal Nonnegative Matrix Factorization (ONMF) aims to approximate a nonnegative matrix as the product of two k-dimensional nonnegative factors, one of which has orthonormal columns. It yields potentially useful data representations as superposition of disjoint parts, while it has been shown to work ll for clustering tasks where traditional methods underperform. Existing algorithms rely mostly on heuristics, which despite their good empirical performance, lack provable performance guarantees.#R##N##R##N# present a new ONMF algorithm with provable approximation guarantees. For any constant dimension k,  obtain an additive EPTAS without any assumptions on the input. Our algorithm relies on a novel approximation to the related Non-negative Principal Component Analysis (NNPCA) problem; given an arbitrary data matrix, NNPCA seeks k nonnegative components that jointly capture most of the variance. Our NNPCA algorithm is of independent interest and generalizes previous work that could only obtain guarantees for a single component.#R##N##R##N# evaluate our algorithms on several real and synthetic datasets and show that their performance matches or outperforms the state of the art."}]